diff --git a/.DESCRIPTION.md b/.DESCRIPTION.md new file mode 100644 index 000000000..8e48aa2a8 --- /dev/null +++ b/.DESCRIPTION.md @@ -0,0 +1,54 @@ +# LAMMPS tutorials + +This is the repository of the [LAMMPS tutorials](https://lammpstutorials.github.io/) +webpage. All the LAMMPS input scripts and data files can be found in a separate +repository named [lammpstutorials-inputs](https://github.com/lammpstutorials/lammpstutorials-inputs). + +The tutorials are compatible with the XXXX2024 release of LAMMPS. + +## About LAMMPS tutorials + +The LAMMPStutorials website is made of seven tutorials that are ordered by increasing difficulty. +[Lennard-Jones fluid](https://lammpstutorials.github.io/sphinx/build/html/tutorials/level1/lennard-jones-fluid.html) +is meant for absolute LAMMPS and molecular dynamics beginners, and the complexity of the simulation is +progressively increased for [Pulling on a carbon nanotube](https://lammpstutorials.github.io/sphinx/build/html/tutorials/level1/breaking-a-carbon-nanotube.html), +[Polymer in water](https://lammpstutorials.github.io/sphinx/build/html/tutorials/level2/polymer-in-water.html), +[Nanosheared electrolyte](https://lammpstutorials.github.io/sphinx/build/html/tutorials/level2/nanosheared-electrolyte.html), +and [Reactive silicon dioxide](https://lammpstutorials.github.io/sphinx/build/html/tutorials/level3/reactive-silicon-dioxide.html). +Finally, [Water adsorption in silica](https://lammpstutorials.github.io/sphinx/build/html/tutorials/level3/water-adsorption-in-silica.html) and +[Free energy calculation](https://lammpstutorials.github.io/sphinx/build/html/tutorials/level3/free-energy-calculation.html) use some more advanced simulation methods that are commonly used when studying soft matter systems, respectively grand canonical Monte Carlo simulations and a free energy method named umbrella sampling. + +

+ + + + + + + + + +

+ +## Access the files + +You can access all the files by cloning this repository with its submodules: + +``` +git clone https://github.com/lammpstutorials/lammpstutorials.github.io.git --recurse-submodule +``` + +Alternatively, you can download the [inputs](https://github.com/lammpstutorials/lammpstutorials-inputs) only: + +``` +git clone https://github.com/lammpstutorials/lammpstutorials.github.io.git +``` + +The Matplotlib Pyplot functions for the figures are shared [here](https://github.com/simongravelle/pyplot-perso). + +### Template ### + +The template from the first page has been adapted from [HTML5 UP](https://html5up.net/). +The other pages use the [Sphinx](https://www.sphinx-doc.org/) generator with the +[furo style](https://github.com/pradyunsg/furo). + diff --git a/.dependencies/.github b/.dependencies/.github new file mode 160000 index 000000000..3f752d63a --- /dev/null +++ b/.dependencies/.github @@ -0,0 +1 @@ +Subproject commit 3f752d63a17fbdf36dd2ea0651bd4ca34b266c35 diff --git a/.dependencies/lammpstutorials-inputs b/.dependencies/lammpstutorials-inputs new file mode 160000 index 000000000..5cf1ff474 --- /dev/null +++ b/.dependencies/lammpstutorials-inputs @@ -0,0 +1 @@ +Subproject commit 5cf1ff474d31a377d1cb96e4dbc838b908e48f19 diff --git a/.dependencies/pyplot-perso b/.dependencies/pyplot-perso new file mode 160000 index 000000000..0cedb1cb3 --- /dev/null +++ b/.dependencies/pyplot-perso @@ -0,0 +1 @@ +Subproject commit 0cedb1cb38658e2d69aaa6c5e2b8401f1559ce5f diff --git a/.files.txt b/.files.txt new file mode 100644 index 000000000..e7c05557c --- /dev/null +++ b/.files.txt @@ -0,0 +1,4 @@ +.dependencies/.github/COMMENT.md +.DESCRIPTION.md +.dependencies/.github/AUTHORS.md +.dependencies/.github/ACKNOWLEDGEMENTS.md diff --git a/.generateREADME.sh b/.generateREADME.sh new file mode 100755 index 000000000..b58b6ab5c --- /dev/null +++ b/.generateREADME.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +.dependencies/.github/generateREADME.sh diff --git a/.github/workflows/gh-pages.yml b/.github/workflows/gh-pages.yml index 1b279b845..d396436bf 100644 --- a/.github/workflows/gh-pages.yml +++ b/.github/workflows/gh-pages.yml @@ -35,6 +35,7 @@ jobs: pip install pygments-lammps pip install sphinx-favicon pip install sphinxcontrib.bibtex + pip install sphinx-tabs - name: Build run: | cd docs/sphinx/ diff --git a/.gitignore b/.gitignore index d4ae23996..499381a8b 100644 --- a/.gitignore +++ b/.gitignore @@ -11,10 +11,6 @@ inputs/freeenergy/BiasedSampling/wham-release-2.0.11 docs/sphinx/build -# force the upload of certain dump files -!docs/inputs/vmd/dump.lammpstrj -!docs/inputs/level1/breaking-a-carbon-nanotube/breakable-bonds/dump.lammpstrj - # latex ebook/**/*.aux ebook/**/*.log @@ -28,3 +24,4 @@ ebook/tutorials/* ebook/non-tutorials/* **/untitled.*.ppm +**/untitled.*.ppn \ No newline at end of file diff --git a/.gitmodules b/.gitmodules index d36f85458..3dde73864 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,6 +1,11 @@ -[submodule "docs/lammpstutorials-inputs"] - path = docs/lammpstutorials-inputs - url = git@github.com:lammpstutorials/lammpstutorials-inputs.git -[submodule "docs/sphinx/source/tutorials/figures/pyplot-perso"] - path = docs/sphinx/source/tutorials/figures/pyplot-perso +[submodule ".dependencies/.github"] + path = .dependencies/.github + url = git@github.com:lammpstutorials/.github.git +[submodule ".dependencies/pyplot-perso"] + path = .dependencies/pyplot-perso url = git@github.com:simongravelle/pyplot-perso.git + branch = LAMMPS-livecom +[submodule ".dependencies/lammpstutorials-inputs"] + path = .dependencies/lammpstutorials-inputs + url = git@github.com:lammpstutorials/lammpstutorials-inputs.git + branch = main diff --git a/LICENSE b/LICENSE index f288702d2..06c608dcf 100644 --- a/LICENSE +++ b/LICENSE @@ -1,674 +1,395 @@ - GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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All the LAMMPS input scripts and data files can be found in a -separate repository named [lammpstutorials-inputs](https://github.com/lammpstutorials/lammpstutorials-inputs). +webpage. All the LAMMPS input scripts and data files can be found in a separate +repository named [lammpstutorials-inputs](https://github.com/lammpstutorials/lammpstutorials-inputs). -The tutorials are compatible with the 2Aug2023 stable release of LAMMPS. +The tutorials are compatible with the XXXX2024 release of LAMMPS. An article is currently is preparation and is [visible here](https://github.com/lammpstutorials/lammpstutorials-article). @@ -51,23 +60,29 @@ The Matplotlib Pyplot functions for the figures are shared [here](https://github ### Template ### The template from the first page has been adapted from [HTML5 UP](https://html5up.net/). -The other pages use the [Sphinx](https://www.sphinx-doc.org/) generator with the [furo style](https://github.com/pradyunsg/furo). +The other pages use the [Sphinx](https://www.sphinx-doc.org/) generator with the +[furo style](https://github.com/pradyunsg/furo). + + + +## Authors -### About me & Contact ### +- [Simon Gravelle](https://github.com/simongravelle) (corr. author), + Université Grenoble Alpes, CNRS, LIPhy, 38000 Grenoble, France. +- [Jacob R. Gissinger](https://www.stevens.edu/profile/jgissing), + Stevens Institute of Technology, Hoboken, NJ 07030, USA. +- [Axel Kohlmeyer](https://sites.google.com/site/akohlmey), + Institute for Computational Molecular Science, Temple University, Philadelphia, + PA 19122, USA. -I am a computer physicist in soft matter and fluids at interfaces. You can -find more information on my [personal webpage](https://simongravelle.github.io/). -See the [contact page](https://lammpstutorials.github.io/sphinx/build/html/non-tutorials/contact-me.html). -You can report issues here on Github, or send me an [email](https://simongravelle.github.io/). Your feedback is always appreciated. -### License and Acknowledgments ### +## Acknowledgements -All the LAMMPS inputs/data/parameter files and Python scripts are released under the -GNU general public license v3.0. Feel free to adapt and/or re-publish them. +- Simon Gravelle acknowledges funding from the European Union's Horizon 2020 + research and innovation programme under the Marie Skłodowska-Curie grant + agreement N°101065060. +- Axel Kohlmeyer acknowledges financial support from Sandia National Laboratories + under POs 2149742 and 2407526. -This project has received funding from the European -Union's Horizon 2020 research and innovation programme -under the Marie Skłodowska-Curie grant agreement No 101065060. -![Acknowledgments-logos](https://raw.githubusercontent.com/simongravelle/credits/1c44b5ae76a33c5bbbd33a54243365c6abdc24b2/cnrs-uga-liphy-msca.png) diff --git a/docs/avatars/level1/breaking-a-carbon-nanotube/avatar-CNT-LAMMPS.png b/docs/avatars/avatar-CNT-LAMMPS.png similarity index 100% rename from docs/avatars/level1/breaking-a-carbon-nanotube/avatar-CNT-LAMMPS.png rename to docs/avatars/avatar-CNT-LAMMPS.png diff --git a/docs/avatars/level3/water-adsorption-in-silica/avatar-GCMC-LAMMPS.png b/docs/avatars/avatar-GCMC-LAMMPS.png similarity index 100% rename from docs/avatars/level3/water-adsorption-in-silica/avatar-GCMC-LAMMPS.png rename to docs/avatars/avatar-GCMC-LAMMPS.png diff --git a/docs/avatars/level1/lennard-jones-fluid/avatar-Lennard-Jones-LAMMPS.png b/docs/avatars/avatar-Lennard-Jones-LAMMPS.png similarity index 100% rename from docs/avatars/level1/lennard-jones-fluid/avatar-Lennard-Jones-LAMMPS.png rename to docs/avatars/avatar-Lennard-Jones-LAMMPS.png diff --git a/docs/avatars/avatar-template.png b/docs/avatars/avatar-template.png deleted file mode 100644 index aa3866e86..000000000 Binary files a/docs/avatars/avatar-template.png and /dev/null differ diff --git a/docs/avatars/color1.png b/docs/avatars/color1.png deleted file mode 100644 index 35dcfedec..000000000 Binary files a/docs/avatars/color1.png and /dev/null differ diff --git a/docs/avatars/color2.png b/docs/avatars/color2.png deleted file mode 100644 index b3e3975be..000000000 Binary files a/docs/avatars/color2.png and /dev/null differ diff --git a/docs/avatars/color3.png b/docs/avatars/color3.png deleted file mode 100644 index bd0c4170b..000000000 Binary files a/docs/avatars/color3.png and /dev/null differ diff --git a/docs/avatars/level1/breaking-a-carbon-nanotube/CNT.png b/docs/avatars/level1/breaking-a-carbon-nanotube/CNT.png deleted file mode 100644 index d630618d9..000000000 Binary files a/docs/avatars/level1/breaking-a-carbon-nanotube/CNT.png and /dev/null differ diff --git a/docs/avatars/logo-LAMMPS.png b/docs/avatars/logo-LAMMPS.png deleted file mode 100644 index 48f049d0f..000000000 Binary files a/docs/avatars/logo-LAMMPS.png and /dev/null differ diff --git a/docs/avatars/material.png b/docs/avatars/material.png deleted file mode 100644 index dc9f6b5f0..000000000 Binary files a/docs/avatars/material.png and /dev/null differ diff --git a/docs/index.html b/docs/index.html index 5e50f3cee..b898b5926 100644 --- a/docs/index.html +++ b/docs/index.html @@ -23,7 +23,7 @@

LAMMPS tutorials

by - Simon Gravelle, CNRS, UGA, LIPhy, Grenoble, France + Simon Gravelle, Jacob R. Gissinger, and Axel Kohlmeyer


@@ -32,9 +32,9 @@

- + - +

Adsorbing water in a cracked silica block @@ -143,12 +143,6 @@

Youtube -
  • - - Twitter - -
  • diff --git a/docs/sphinx/compile.py b/docs/sphinx/compile.py new file mode 100644 index 000000000..a9d9ebc47 --- /dev/null +++ b/docs/sphinx/compile.py @@ -0,0 +1,53 @@ +import subprocess +import re + +# Define ANSI escape codes for colors +RED = '\033[91m' +GREEN = '\033[92m' +GRAY = '\033[90m' +RESET = '\033[0m' + +def filter_make_output(): + + # Define multiple patterns to ignore + ignore_patterns = [ + "Pygments lexer name 'bw' is not known", + "Pygments lexer name 'lammps' is not known", + ".. label:: start_", + ".. label:: end_", + "Unknown directive type" + ] + + # Define a pattern to identify warnings (example pattern, adjust as needed) + warning_pattern = re.compile('|'.join(re.escape(p) for p in ["WARNING:", "ERROR:"])) + + # Combine ignore patterns into a single regex pattern + ignore_pattern = re.compile('|'.join(re.escape(p) for p in ignore_patterns)) + + # Run 'make clean' + subprocess.run(['make', 'clean'], check=True) + + # Run 'make html' and capture output + process = subprocess.Popen(['make', 'html'], + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT, text=True) + + # Read and filter output + output_lines = [] + for line in process.stdout: + if len(line) > 1: + if not ignore_pattern.search(line): + # Determine the color based on whether the line matches the warning pattern + if warning_pattern.search(line): + output_lines.append(RED + line + RESET) + else: + output_lines.append(GRAY + line + RESET) + + # Wait for the process to complete + process.wait() + + # Print the filtered output + print(''.join(output_lines), end='') + +if __name__ == "__main__": + filter_make_output() diff --git a/docs/sphinx/source/_static/custom.css b/docs/sphinx/source/_static/custom.css index 978835282..0cfc366d5 100644 --- a/docs/sphinx/source/_static/custom.css +++ b/docs/sphinx/source/_static/custom.css @@ -47,6 +47,27 @@ mask-image: var(--icon--info); } +/* New non-title information admonition */ +.admonition.non-title-info > .admonition-title { + display: none; /* Hide the title */ + } + + .admonition.non-title-info { + background-color: #2962ff10; + border-color: #2963ff; + font-size: 13pt; + } + + .admonition.non-title-info > .admonition-content { + color: #4b2d83; /* Purple text color for contrast */ + } + + .admonition.non-title-info > .admonition-title::before { + content: ''; /* No icon needed, as there's no title */ + } + + + :root { /*https://fontawesomeicons.com/svg/icons/info*/ --icon--patreon: url('data:image/svg+xml;charset=utf-8, ') } @@ -114,6 +135,23 @@ mask-image: var(--icon--solution); } +:root { + --icon--gui: url('data:image/svg+xml;charset=utf-8, ') +} + +.admonition.gui > .admonition-title { + background-color: #2962ff10; + border-color: #2963ff; + font-size: 13pt; +} + +.admonition.gui > .admonition-title::before { + background-color: #2963ff; + -webkit-mask-image: var(--icon--gui); + mask-image: var(--icon--gui); +} + + a:link { text-decoration: none; } @@ -126,4 +164,18 @@ a:hover { } a:active { text-decoration: none; +} + +section { + text-align: justify; +} + +code { + border: none; + box-shadow: none; + outline: none; +} + +strong { + font-weight: 550; } \ No newline at end of file diff --git a/docs/sphinx/source/_static/screenshot_readme_lammps_tutorials_1.png b/docs/sphinx/source/_static/screenshot_readme_lammps_tutorials_1.png deleted file mode 100644 index 3fc67f10f..000000000 Binary files a/docs/sphinx/source/_static/screenshot_readme_lammps_tutorials_1.png and /dev/null differ diff --git a/docs/sphinx/source/_static/screenshot_readme_lammps_tutorials_2.png b/docs/sphinx/source/_static/screenshot_readme_lammps_tutorials_2.png deleted file mode 100644 index ea9cac1aa..000000000 Binary files a/docs/sphinx/source/_static/screenshot_readme_lammps_tutorials_2.png and /dev/null differ diff --git a/docs/sphinx/source/conf.py b/docs/sphinx/source/conf.py index 613d010b9..bc6cac64b 100644 --- a/docs/sphinx/source/conf.py +++ b/docs/sphinx/source/conf.py @@ -1,7 +1,7 @@ # Configuration file for the Sphinx documentation builder. copyright = 'All source code is available under the GNU General Public License v3.0' -author = 'Simon Gravelle' +author = 'See the AUTHORS.rst' extensions = ['sphinx_togglebutton', 'sphinx_favicon', @@ -31,4 +31,4 @@ html_show_sphinx = False html_short_title = "True" -bibtex_bibfiles = ['journal-article.bib'] \ No newline at end of file +bibtex_bibfiles = ['journal-article.bib'] diff --git a/docs/sphinx/source/gromacs_replace.html b/docs/sphinx/source/gromacs_replace.html deleted file mode 100644 index 44b4bdb8b..000000000 --- a/docs/sphinx/source/gromacs_replace.html +++ /dev/null @@ -1,11 +0,0 @@ - - - - Use GROMACS instead - - - -

    Redirecting you to the GROMACS tutorials webpage, - the companion website of LAMMPS tutorials

    - - \ No newline at end of file diff --git a/docs/sphinx/source/index.rst b/docs/sphinx/source/index.rst index acd7970c9..5879eb0fa 100644 --- a/docs/sphinx/source/index.rst +++ b/docs/sphinx/source/index.rst @@ -6,38 +6,21 @@ lammpstutorials by Simon Gravelle : LAMMPS courses for beginners .. toctree:: :maxdepth: 2 - non-tutorials/before-you-start.rst - non-tutorials/contact-me.rst + non-tutorials/scope.rst + non-tutorials/prerequisites.rst + non-tutorials/contact.rst .. toctree:: :maxdepth: 2 - :caption: Level 1 + :caption: Tutorials - tutorials/level1/lennard-jones-fluid.rst - tutorials/level1/breaking-a-carbon-nanotube.rst - -.. toctree:: - :maxdepth: 2 - :caption: Level 2 - - tutorials/level2/polymer-in-water.rst - tutorials/level2/nanosheared-electrolyte.rst - -.. toctree:: - :maxdepth: 2 - :caption: Level 3 - - tutorials/level3/reactive-silicon-dioxide.rst - tutorials/level3/water-adsorption-in-silica.rst - tutorials/level3/free-energy-calculation.rst - -.. toctree:: - :maxdepth: 2 - :caption: Extra tools - - tutorials/vmd/vmd-tutorial.rst - tutorials/mdanalysis/mdanalysis-tutorial.rst - tutorials/bash/bash-tutorial.rst + tutorial1/lennard-jones-fluid.rst + tutorial2/breaking-a-carbon-nanotube.rst + tutorial3/polymer-in-water.rst + tutorial4/nanosheared-electrolyte.rst + tutorial5/reactive-silicon-dioxide.rst + tutorial6/water-adsorption-in-silica.rst + tutorial7/free-energy-calculation.rst .. toctree:: :maxdepth: 2 @@ -46,7 +29,9 @@ lammpstutorials by Simon Gravelle : LAMMPS courses for beginners non-tutorials/solutions.rst non-tutorials/glossary.rst non-tutorials/bibliography.rst - miscellaneous/use-gromacs-instead.rst + non-tutorials/using-lammps-gui.rst + non-tutorials/running-lammps.rst + non-tutorials/command-line.rst Indices and tables ================== diff --git a/docs/sphinx/source/index_replace.html b/docs/sphinx/source/index_replace.html index c3698132f..e6d93463d 100644 --- a/docs/sphinx/source/index_replace.html +++ b/docs/sphinx/source/index_replace.html @@ -1,15 +1,8 @@ - Main index - old + Main index - old - -

    Redirecting you to the main page of LAMMPS tutorials

    diff --git a/docs/sphinx/source/journal-article.bib b/docs/sphinx/source/journal-article.bib index 4081446f8..0c7ea46aa 100644 --- a/docs/sphinx/source/journal-article.bib +++ b/docs/sphinx/source/journal-article.bib @@ -1,3 +1,44 @@ +@article{schneider1978molecular, + title={Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions}, + author={Schneider, T and Stoll, E}, + journal={Physical Review B}, + volume={17}, + number={3}, + pages={1302}, + year={1978}, + publisher={APS} +} + +@book{hestenes1952methods, + title={Methods of conjugate gradients for solving linear systems}, + author={Hestenes, Magnus Rudolph and Stiefel, Eduard and others}, + volume={49}, + number={1}, + year={1952}, + publisher={NBS Washington, DC} +} + +@article{cauchy1847methode, + title={M{\'e}thode g{\'e}n{\'e}rale pour la r{\'e}solution des systemes d'{\'e}quations simultan{\'e}es}, + author={Cauchy, Augustin and others}, + journal={Comp. Rend. Sci. Paris}, + volume={25}, + number={1847}, + pages={536--538}, + year={1847} +} + +@article{stukowski2009visualization, + title={Visualization and analysis of atomistic simulation data with OVITO--the Open Visualization Tool}, + author={Stukowski, Alexander}, + journal={Modelling and simulation in materials science and engineering}, + volume={18}, + number={1}, + pages={015012}, + year={2009}, + publisher={IOP Publishing} +} + @book{barrat2003basic, title={Basic concepts for simple and complex liquids}, author={Barrat, Jean-Louis and Hansen, Jean-Pierre}, @@ -5,16 +46,81 @@ @book{barrat2003basic publisher={Cambridge University Press} } -@article{gravelle2025tutorials, - author = {Simon Gravelle and Jacob R. Gissinger and Axel Kohlmeyer}, - title = {A Set of Tutorials for the LAMMPS Simulation Package}, - journal = {arXiv preprint}, - year = {2025}, - archivePrefix = {arXiv}, - eprint = {2503.14020}, - primaryClass = {physics.comp-ph}, - doi = {10.48550/arXiv.2503.14020}, - note = {Submitted on 18 Mar 2025} +@article{ewald1921berechnung, + title={Die Berechnung optischer und elektrostatischer Gitterpotentiale}, + author={Ewald, Paul P}, + journal={Annalen der physik}, + volume={369}, + number={3}, + pages={253--287}, + year={1921}, + publisher={Wiley Online Library} +} + +@book{frenkel2023understanding, + title={Understanding molecular simulation: from algorithms to applications}, + author={Frenkel, Daan and Smit, Berend}, + year={2023}, + publisher={Elsevier} +} + +@book{van1995python, + title={Python reference manual}, + author={van Rossum, Guido and Drake Jr, Fred L}, + year={1995}, + publisher={Centrum voor Wiskunde en Informatica Amsterdam} +} + +@article{humphrey1996vmd, + title={{{VMD}}: visual molecular dynamics}, + author={Humphrey, William and Dalke, Andrew and Schulten, Klaus}, + journal={Journal of molecular graphics}, + volume={14}, + number={1}, + pages={33--38}, + year={1996}, + publisher={Elsevier} +} + +@book{hansen2013theory, + title={Theory of simple liquids: with applications to soft matter}, + author={Hansen, Jean-Pierre and McDonald, Ian Ranald}, + year={2013}, + publisher={Academic press} +} + +@article{della1992molecular, + title={Molecular dynamics simulation of silica liquid and glass}, + author={Della Valle, Raffaele Guido and Andersen, Hans C}, + journal={The Journal of chemical physics}, + volume={97}, + number={4}, + pages={2682--2689}, + year={1992}, + publisher={American Institute of Physics} +} + +@article{mills1955remeasurement, + title={A remeasurement of the self-diffusion coefficients of sodium ion in aqueous sodium chloride solutions}, + author={Mills, Reginald}, + journal={Journal of the American Chemical Society}, + volume={77}, + number={23}, + pages={6116--6119}, + year={1955}, + publisher={ACS Publications} + +} + +@article{gissinger2024type, + title={Type label framework for bonded force fields in LAMMPS}, + author={Gissinger, Jacob R and Nikiforov, Ilia and Afshar, Yaser and Waters, Brendon and Choi, Moon-ki and Karls, Daniel S and Stukowski, Alexander and Im, Wonpil and Heinz, Hendrik and Kohlmeyer, Axel and others}, + journal={The Journal of Physical Chemistry B}, + volume={128}, + number={13}, + pages={3282--3297}, + year={2024}, + publisher={ACS Publications} } @article{sulpizi2012silica, @@ -130,7 +236,6 @@ @article{wolde-kidanInterplayInterfacialViscosity2021 title = {Interplay of {{Interfacial Viscosity}}, {{Specific-Ion}}, and {{Impurity Adsorption Determines Zeta Potentials}} of {{Phospholipid Membranes}}}, author = {{Wolde-Kidan}, Amanuel and Netz, Roland R.}, year = {2021}, - month = jul, journal = {Langmuir}, volume = {37}, number = {28}, @@ -225,15 +330,6 @@ @article{fischer2023history publisher={Elsevier} } -@book{hestenes1952methods, - title={Methods of conjugate gradients for solving linear systems}, - author={Hestenes, Magnus Rudolph and Stiefel, Eduard and others}, - volume={49}, - number={1}, - year={1952}, - publisher={NBS Washington, DC} -} - @article{wong2016good, title={The good, the bad and the user in soft matter simulations}, author={Wong-Ekkabut, Jirasak and Karttunen, Mikko}, @@ -256,13 +352,6 @@ @article{wang2020lennard publisher={Royal Society of Chemistry} } -@book{hansen2013theory, - title={Theory of simple liquids: with applications to soft matter}, - author={Hansen, Jean-Pierre and McDonald, Ian Ranald}, - year={2013}, - publisher={Academic press} -} - @book{allen2017computer, title={Computer simulation of liquids}, author={Allen, Michael P and Tildesley, Dominic J}, @@ -291,24 +380,6 @@ @article{thompson2022lammps publisher={Elsevier} } -@book{van1995python, - title={Python reference manual}, - author={van Rossum, Guido and Drake Jr, Fred L}, - year={1995}, - publisher={Centrum voor Wiskunde en Informatica Amsterdam} -} - -@article{humphrey1996vmd, - title={{{VMD}}: visual molecular dynamics}, - author={Humphrey, William and Dalke, Andrew and Schulten, Klaus}, - journal={Journal of molecular graphics}, - volume={14}, - number={1}, - pages={33--38}, - year={1996}, - publisher={Elsevier} -} - @inproceedings{gowers2016mdanalysis, title={{{MDAnalysis}}: a {{Python}} package for the rapid analysis of molecular dynamics simulations}, author={Gowers, Richard J and Linke, Max and Barnoud, Jonathan and Reddy, Tyler JE and Melo, Manuel N and Seyler, Sean L and Domanski, Jan and Dotson, David L and Buchoux, S{\'e}bastien and Kenney, Ian M and others}, @@ -348,7 +419,7 @@ @article{hunter2007Matplotlib Number = {3}, Pages = {90--95}, publisher = {IEEE COMPUTER SOC}, - year = 2007 + year = {2007} } @article{lee2008molecular, @@ -381,33 +452,16 @@ @article{wu2006flexible publisher={AIP Publishing} } -@article{cauchy1847methode, - title={M{\'e}thode g{\'e}n{\'e}rale pour la r{\'e}solution des systemes d'{\'e}quations simultan{\'e}es}, - author={Cauchy, Augustin and others}, - journal={Comp. Rend. Sci. Paris}, - volume={25}, - number={1847}, - pages={536--538}, - year={1847} -} - @misc{sveinsson2021logfile, title = {{{LAMMPS}} logfile reader}, author = {Henrik Andersen Sveinsson}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, - howpublished = {\url{https://github.com/henriasv/lammps-logfile}}, + url = {\url{https://github.com/henriasv/lammps-logfile}}, commit = {b7e87d7} } -@book{frenkel2023understanding, - title={Understanding molecular simulation: from algorithms to applications}, - author={Frenkel, Daan and Smit, Berend}, - year={2023}, - publisher={Elsevier} -} - @misc{kohlmeyer2017topotools, title={{{TopoTools}}: {{Release}} 1.9}, author={Kohlmeyer, Axel and Vermaas, Josh}, @@ -419,7 +473,7 @@ @misc{kohlmeyer2017topotools @misc{grossfieldimplementation, title={An implementation of {{WHAM}}: the Weighted Histogram Analysis Method Version 2.0.10}, author={Grossfield, Alan}, - url= { http://membrane.urmc.rochester.edu/content/wham/} + url= {http://membrane.urmc.rochester.edu/content/wham/} } @article{vashishta1990interaction, @@ -455,6 +509,17 @@ @article{liese2017hydration publisher={ACS Publications} } +@article{kadaoluwa2021systematic, + title={Systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations}, + author={Kadaoluwa Pathirannahalage, Sachini P and Meftahi, Nastaran and Elbourne, Aaron and Weiss, Alessia CG and McConville, Chris F and Padua, Agilio and Winkler, David A and Costa Gomes, Margarida and Greaves, Tamar L and Le, Tu C and others}, + journal={Journal of Chemical Information and Modeling}, + volume={61}, + number={9}, + pages={4521--4536}, + year={2021}, + publisher={ACS Publications} +} + @article{abascal2005general, title={A general purpose model for the condensed phases of water: {{TIP4P/2005}}}, author={Abascal, Jose LF and Vega, Carlos}, @@ -508,15 +573,80 @@ @article{berendsen1984molecular publisher={American Institute of Physics} } -@article{schneider1978molecular, - title={Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions}, - author={Schneider, T and Stoll, E}, - journal={Physical Review B}, - volume={17}, - number={3}, - pages={1302}, - year={1978}, - publisher={APS} +@article{sun1998compass, + title={COMPASS: an ab initio force-field optimized for condensed-phase + applications overview with details on alkane and benzene compounds}, + author={Sun, Huai}, + journal={The Journal of Physical Chemistry B}, + volume={102}, + number={38}, + pages={7338--7364}, + year={1998}, + publisher={ACS Publications} +} + +@article{gissinger2020reacter, + title={REACTER: A heuristic method for reactive molecular dynamics}, + author={Gissinger, Jacob R and Jensen, Benjamin D and Wise, Kristopher E}, + journal={Macromolecules}, + volume={53}, + number={22}, + pages={9953--9961}, + year={2020}, + publisher={ACS Publications} +} + +@article{kumar1995multidim, + title={Multidimensional free-energy calculations using the weighted histogram analysis method}, + author={Kumar, Shankar and Rosenberg, John M and Bouzida, Djamal and Swendsen, Robert H and Kollman, Peter A}, + journal={Journal of Computational Chemistry}, + volume={16}, + number={11}, + pages={1339--1350}, + year={1995}, + publisher={Wiley Online Library} +} + +@article{hayatifar2024probing, + title={Probing atomic-scale processes at the ferrihydrite-water interface with reactive molecular dynamics}, + author={Hayatifar, Ardalan and Gravelle, Simon and Moreno, Beatriz D and Schoepfer, Valerie A and Lindsay, Matthew BJ}, + journal={Geochemical Transactions}, + volume={25}, + number={1}, + pages={10}, + year={2024}, + publisher={Springer} +} + +@article{loche2022molecular, + title={Molecular dynamics simulations of the evaporation of hydrated ions from aqueous solution}, + author={Loche, Philip and Bonthuis, Douwe J and Netz, Roland R}, + journal={Communications Chemistry}, + volume={5}, + number={1}, + pages={55}, + year={2022}, + publisher={Nature Publishing Group UK London} +} + +@article{gissinger2017polymer, +title = {Modeling chemical reactions in classical molecular dynamics simulations}, +journal = {Polymer}, +volume = {128}, +pages = {211-217}, +year = {2017}, +issn = {0032-3861}, +author = {Jacob R. Gissinger and Benjamin D. Jensen and Kristopher E. Wise} +} + +@article{gissinger2024molecular, + title={Molecular modeling of reactive systems with REACTER}, + author={Gissinger, Jacob R and Jensen, Benjamin D and Wise, Kristopher E}, + journal={Computer Physics Communications}, + volume={304}, + pages={109287}, + year={2024}, + publisher={Elsevier} } @article{mackerell2000development, @@ -530,6 +660,13 @@ @article{mackerell2000development publisher={Wiley Online Library} } +@article{gravelle2025tutorials, + title={A Set of Tutorials for the LAMMPS Simulation Package}, + author={Gravelle, Simon and Gissinger, Jacob R and Kohlmeyer, Axel}, + journal={arXiv preprint arXiv:2503.14020}, + year={2025} +} + @article{zou2012investigation, title={Investigation of complex iron surface catalytic chemistry using the {{ReaxFF}} reactive force field method}, author={Zou, Chenyu and van Duin, Adri}, diff --git a/docs/sphinx/source/miscellaneous/figures/use-gromacs-instead/gromacs-dark.png b/docs/sphinx/source/miscellaneous/figures/use-gromacs-instead/gromacs-dark.png deleted file mode 100644 index 4aed1e187..000000000 Binary files a/docs/sphinx/source/miscellaneous/figures/use-gromacs-instead/gromacs-dark.png and /dev/null differ diff --git a/docs/sphinx/source/miscellaneous/figures/use-gromacs-instead/gromacs-light.png b/docs/sphinx/source/miscellaneous/figures/use-gromacs-instead/gromacs-light.png deleted file mode 100644 index 3d694cd70..000000000 Binary files a/docs/sphinx/source/miscellaneous/figures/use-gromacs-instead/gromacs-light.png and /dev/null differ diff --git a/docs/sphinx/source/miscellaneous/use-gromacs-instead.rst b/docs/sphinx/source/miscellaneous/use-gromacs-instead.rst deleted file mode 100644 index 09ac2d1dc..000000000 --- a/docs/sphinx/source/miscellaneous/use-gromacs-instead.rst +++ /dev/null @@ -1,25 +0,0 @@ -.. gromacs-label: - -Use GROMACS instead -******************* - -.. figure:: figures/use-gromacs-instead/gromacs-dark.png - :alt: logo for GROMACS, an alternative to LAMMPS - :height: 250 - :align: right - :class: only-dark - -.. figure:: figures/use-gromacs-instead/gromacs-light.png - :alt: logo for GROMACS, an alternative to LAMMPS - :height: 250 - :align: right - :class: only-light - -|gromacstutorials| is another molecular dynamics package. - -Although less flexible than LAMMPS, GROMACS can be more than **10 times faster**, -which makes it a powerful alternative to LAMMPS in some situations. - -.. |gromacstutorials| raw:: html - -     GROMACS diff --git a/docs/sphinx/source/non-tutorials/.contact-me.rst b/docs/sphinx/source/non-tutorials/.contact-me.rst deleted file mode 100644 index 598550931..000000000 --- a/docs/sphinx/source/non-tutorials/.contact-me.rst +++ /dev/null @@ -1,67 +0,0 @@ -.. _old_contact-label: - -Contact me -********** - -.. container:: justify - - Feel free to contact me by `email`_ and - ask me any LAMMPS-related questions. - -.. _email: simon.gravelle@live.fr - -Looking for help? -================= - -.. figure:: figures/contact-me/patreon.png - :height: 100 - :alt: Simon gravelle patreon for LAMMPS and GROMACS material - :align: right - :target: https://www.patreon.com/molecularsimulations - -.. container:: justify - - If you are looking for support in setting-up your - molecular simulations, you can become a |patreon|, and: - - - Receive **personalized advice** for your project - - Have your LAMMPS **inputs reviewed** - - Access a dedicated Discord forum - - Join a community of 50+ members - -Support the project -=================== - -.. container:: justify - - You can support the project financially by registering on |patreon| - or by donating on |tipeee|. - -.. |patreon| raw:: html - - Patreon - -.. |tipeee| raw:: html - - tipeee - -About me -======== - -.. container:: justify - - I have been using LAMMPS for research and education since 2012, see my |personal_page_simon_gravelle|. - Follow me on |twitter_simon_gravelle|, where I post news about these tutorials - and my research or follow me on |github_simon_gravelle|, where I share more molecular simulation content. - -.. |personal_page_simon_gravelle| raw:: html - - personal page - -.. |twitter_simon_gravelle| raw:: html - - twitter - -.. |github_simon_gravelle| raw:: html - - github \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/1.9.2.rst b/docs/sphinx/source/non-tutorials/1.9.2.rst deleted file mode 100644 index bf7eb0cc8..000000000 --- a/docs/sphinx/source/non-tutorials/1.9.2.rst +++ /dev/null @@ -1,3 +0,0 @@ -.. container:: version - - This tutorial was tested with the 1.9.3 VMD version. \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/2Aug2023.rst b/docs/sphinx/source/non-tutorials/2Aug2023.rst deleted file mode 100644 index c6e23bb09..000000000 --- a/docs/sphinx/source/non-tutorials/2Aug2023.rst +++ /dev/null @@ -1,3 +0,0 @@ -.. container:: version - - This tutorial is compatible with the 2Aug2023 LAMMPS version. \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/before-you-start.rst b/docs/sphinx/source/non-tutorials/before-you-start.rst deleted file mode 100644 index 8b1f3e561..000000000 --- a/docs/sphinx/source/non-tutorials/before-you-start.rst +++ /dev/null @@ -1,144 +0,0 @@ -.. _contact-before-you-start-label: - -Before you start -**************** - -.. container:: justify - - *LAMMPS tutorials* is made of seven tutorials that are - ordered by increasing difficulty. The first tutorial, :ref:`lennard-jones-label`, - is meant for LAMMPS absolute beginners. The complexity - of the simulations is then progressively increased - for :ref:`carbon-nanotube-label`, - :ref:`all-atoms-label`, - :ref:`sheared-confined-label`, - and :ref:`reactive-silicon-dioxide-label`. - Finally, in :ref:`gcmc-silica-label` and - :ref:`umbrella-sampling-label`, - some more advanced simulation methods - are used, namely grand canonical Monte Carlo simulations and - a free energy method named umbrella sampling. - -Required software -================= - -LAMMPS (2Aug2023) -------------------- - -.. container:: justify - - Download and install LAMMPS version 2Aug2023 by following the - instructions of the |LAMMPS website installation| :cite:`thompson2022lammps`. - Depending on your operating system (i.e. Linux, macOS, or Windows), - the procedure may differ. - -.. |LAMMPS website installation| raw:: html - - LAMMPS website - -.. container:: justify - - LAMMPS must be compiled with the following packages: - -.. container:: justify - - - MANYBODY - - MOLECULE - - KSPACE - - RIGID - - REAXFF - - EXTRA-DUMP - -.. container:: justify - - If you decide to use another LAMMPS version, certain commands - may not work and LAMMPS will throw an |LAMMPS error|. - -.. |LAMMPS error| raw:: html - - error message - -VMD (optional) --------------- - -.. container:: justify - - To visualize the simulation, |VMD| version 1.9.3 will - be used :cite:`humphrey1996vmd`. Some basic instructions for VMD are given here in the - :ref:`vmd-label`. Feel free to use an alternative visualization - software like |Ovito|. - -.. |VMD| raw:: html - - VMD - -.. |Ovito| raw:: html - - Ovito - -Python (optional) ------------------ - -.. container:: justify - - To perform post-mortem analysis of the data during the :ref:`mda-label`, - MDAnalysis version 2.6.1 is used - together with Python version 3.11.4 - :cite:`van1995python, michaud2011mdanalysis, gowers2016mdanalysis`. - -.. container:: justify - - To plot the results from the simulations, - |Matplotlib Pyplot| version 3.5.2 is used - in combination with |lammps_logfile|, a library allowing - one to read the *log* file produced by LAMMPS :cite:`hunter2007Matplotlib, sveinsson2021logfile`. - -.. |Matplotlib Pyplot| raw:: html - - Matplotlib Pyplot - -.. |lammps_logfile| raw:: html - - lammps logfile - -Text editing software ---------------------- - -.. container:: justify - - To write and edit LAMMPS input files, a text editor is required. - Any text editor will do, such as |gedit|, |vim|, - or |vscode|. - -.. |gedit| raw:: html - - gedit - -.. |vim| raw:: html - - vim - -.. |vscode| raw:: html - - vscode - -Find the input scripts -====================== - -.. include:: ../non-tutorials/accessfile.rst - -Recommended reading -=================== - -.. container:: justify - - To better understand molecular dynamics simulations, I recommend the reading - of *Understanding molecular simulation* by Daan Frenkel and Berend - Smit :cite:`frenkel2023understanding`, as well as - *Computer simulation of liquids* by Michael Allen and Dominic Tildesley - :cite:`allen2017computer`. To understand the basic concepts - of fluid and Soft Matter systems, I recommend reading *Basic concepts for - simple and complex liquids* by Jean-Louis Barrat and Jean-Pierre Hansen - :cite:`barrat2003basic`, - as well as *Theory of simple liquids: with applications to soft matter* - by Jean-Pierre Hansen and Ian Ranald McDonald :cite:`hansen2013theory`. \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/command-line.rst b/docs/sphinx/source/non-tutorials/command-line.rst new file mode 100644 index 000000000..2473564b5 --- /dev/null +++ b/docs/sphinx/source/non-tutorials/command-line.rst @@ -0,0 +1,42 @@ +.. _command-line-label: + +Command line +============ + +LAMMPS can also be executed from the command-line on Linux, macOS, and +Windows without using the GUI. This is the more common way to run LAMMPS. +Both, the LAMMPS--GUI program and the LAMMPS command-line executable +utilize the same LAMMPS library and thus no changes to the input file are required. + +First, open a terminal or command-line prompt window and navigate to the +directory containing the **input.lmp** file. Then execute: + +.. code-block:: bash + + lmp -in input.lmp + +where ``lmp`` is the command-line LAMMPS command. + +For parallel execution with 4 processors (via OpenMP threads where supported +by the OPENMP package), use: + +.. code-block:: bash + + lmp -in input.lmp -pk omp 4 -sf omp + +.. admonition:: Note + :class: non-title-info + + Running in parallel via MPI requires a specially compiled LAMMPS + package and is not supported by the GUI. On supercomputers or HPC + clusters, pre-compiled LAMMPS executables are typically provided + by the facility's user support team. For more information, please + refer to the facility's documentation or contact its user support staff. + +See the |LAMMPSdocumentation| for a complete description on how to +run LAMMPS. + +.. |LAMMPSdocumentation| raw:: html + + LAMMPS documentation + diff --git a/docs/sphinx/source/non-tutorials/contact-me.rst b/docs/sphinx/source/non-tutorials/contact-me.rst deleted file mode 100644 index d83d6a7ac..000000000 --- a/docs/sphinx/source/non-tutorials/contact-me.rst +++ /dev/null @@ -1,85 +0,0 @@ -.. _contact-label: - -Looking for help? -***************** - -If you're struggling with your simulations, here are a few options to consider: - -- Send me an email (find my address |simon-gravelle|). Please note that I receive - numerous queries, and while I do my best to answer all of them, I prioritize - requests where you introduce yourself and ask a clear, concise question. - -- Join my |Discord| server, where you can ask questions and engage with me and other users. - -- Try your luck on the official |MatSci| forum for LAMMPS. Please note that - the main purpose of the forum is to report LAMMPS bugs to developers, rather - than to seek advice on research questions. - -.. |simon-gravelle| raw:: html - - here - -.. |Discord| raw:: html - - Discord - -.. |MatSci| raw:: html - - MatSci - -You have feedback for us? -========================= - -If you have feedback, you can raise an issue on |GitHub|, especially if you've -noticed any inconsistencies, errors, or typos. - -.. |GitHub| raw:: html - - GitHub - -Support the LAMMPS tutorials initiative -======================================= - -.. container:: justify - - You can support this webpage through |patreon| or |tipeee|. As a supporter, - you will access dedicated support channels on |Discord|. - -.. |patreon| raw:: html - - Patreon - -.. |tipeee| raw:: html - - Tipeee - -Follow us -========= - -.. container:: justify - - - |github_lammps_tutorials| community account - - |twitter_lammps_tutorials| community account - - Creator's |personal_page_simon_gravelle| - - |twitter_simon_gravelle| personal account - - |github_simon_gravelle| personal account - -.. |github_lammps_tutorials| raw:: html - - GitHub - -.. |twitter_lammps_tutorials| raw:: html - - Twitter - -.. |personal_page_simon_gravelle| raw:: html - - personal page - -.. |twitter_simon_gravelle| raw:: html - - Twitter - -.. |github_simon_gravelle| raw:: html - - GitHub \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/contact.rst b/docs/sphinx/source/non-tutorials/contact.rst new file mode 100644 index 000000000..aa6df9cd7 --- /dev/null +++ b/docs/sphinx/source/non-tutorials/contact.rst @@ -0,0 +1,47 @@ +.. _contact-label: + +Contact +******* + +Since its launch in 2019, this website has evolved considerably--and will +likely continue to do so. + +Follow the LAMMPS tutorials initiative +====================================== + +You can follow the progress of LAMMPStutorials via: + +- the |github_lammps_tutorials| account of LAMMPS tutorials, +- the |mastodon_lammps_tutorials| account of LAMMPS tutorials. + +.. |github_lammps_tutorials| raw:: html + + GitHub + +.. |mastodon_lammps_tutorials| raw:: html + + Mastodon + +Contact the founder +=================== + +You can reach the founder of LAMMPS tutorials, |simongravelle_page|, for suggestions, +to report issues with the website, or to ask general questions about LAMMPS +or molecular simulations. + +.. |simongravelle_page| raw:: html + + Simon Gravelle + +Struggling with your simulation project? +======================================== + +Register on |patreon| and receive personalised help for your LAMMPS research project, +or simply support the founder of LAMMPStutorials. + +.. |patreon| raw:: html + + patreon + + + diff --git a/docs/sphinx/source/non-tutorials/figures/contact-me/patreon.png b/docs/sphinx/source/non-tutorials/figures/contact-me/patreon.png deleted file mode 100644 index 98d1aa466..000000000 Binary files a/docs/sphinx/source/non-tutorials/figures/contact-me/patreon.png and /dev/null differ diff --git a/docs/sphinx/source/non-tutorials/figures/contact-me/patreon.svg b/docs/sphinx/source/non-tutorials/figures/contact-me/patreon.svg deleted file mode 100644 index 7b28dceb1..000000000 --- a/docs/sphinx/source/non-tutorials/figures/contact-me/patreon.svg +++ /dev/null @@ -1,271 +0,0 @@ - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - diff --git a/docs/sphinx/source/non-tutorials/link-to-solutions.rst b/docs/sphinx/source/non-tutorials/link-to-solutions.rst deleted file mode 100644 index 69cf4e754..000000000 --- a/docs/sphinx/source/non-tutorials/link-to-solutions.rst +++ /dev/null @@ -1,4 +0,0 @@ -.. container:: justify - - Each exercise comes with a proposed solution, - see :ref:`solutions-label`. \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/needhelp.rst b/docs/sphinx/source/non-tutorials/needhelp.rst deleted file mode 100644 index bc8fe3584..000000000 --- a/docs/sphinx/source/non-tutorials/needhelp.rst +++ /dev/null @@ -1,5 +0,0 @@ - -.. admonition:: Looking for help or have feedback for us? - :class: patreon - - Visit the :ref:`contact-label` page to get in touch with us. diff --git a/docs/sphinx/source/non-tutorials/prerequisites.rst b/docs/sphinx/source/non-tutorials/prerequisites.rst new file mode 100644 index 000000000..4cfc4f514 --- /dev/null +++ b/docs/sphinx/source/non-tutorials/prerequisites.rst @@ -0,0 +1,132 @@ +.. _prerequisites-label: + +Prerequisites +************* + +Background knowledge +==================== + +This set of tutorials assumes no prior knowledge of the LAMMPS software +itself. To complete the tutorials, a text editor and a suitable LAMMPS +executable are required. We use |lammpsguidocs| +here, as it offers features that make it particularly convenient for +tutorials, but other console or graphical text editors, such as GNU +nano, vi/vim, Emacs, Notepad, Gedit, and Visual Studio Code can also be +used. LAMMPS can be executed either directly from LAMMPS--GUI +(:ref:`using-lammps-gui-label`) or from a command prompt +(:ref:`command-line-label`), the latter of which requires some familiarity +with executing commands from a terminal or command-line prompt. + +In addition, prior knowledge of the theoretical basics of molecular +simulations and statistical physics is highly beneficial. Users may +refer to textbooks such as *Understanding Molecular Simulation* by +Daan Frenkel and Berend Smit :cite:`frenkel2023understanding`, as well as +*Computer Simulation of Liquids* by Michael Allen and Dominic +Tildesley :cite:`allen2017computer`. To better understand +the fundamental concepts behind the soft matter systems simulated in these +tutorials, users can also refer to *Basic Concepts for Simple and Complex Liquids* +by Jean-Louis Barrat and Jean-Pierre Hansen +:cite:`barrat2003basic`, as well as +*Theory of Simple Liquids: with Applications to Soft Matter* +by Jean-Pierre Hansen and Ian Ranald McDonald :cite:`hansen2013theory`. +For more resources, the |sklogwiki_main_page| platform provies a wide range of information +on statistical mechanics and molecular simulations. + +.. |sklogwiki_main_page| raw:: html + + SklogWiki + +Software/system requirements +============================ + +The LAMMPS stable release version 29Aug2024 (update2) +and the matching LAMMPS--GUI software version 1.6.12 are required to +follow the tutorials, as they include features that were first +introduced in these versions. For Linux (x86_64 CPU), macOS (BigSur or +later), and Windows (10 and 11) you can download a precompiled LAMMPS +package from the LAMMPS |LAMMPSrelease| page on +GitHub. Select a package with ``GUI`` in the +file name, which includes both, LAMMPS--GUI and the LAMMPS command-line +executable. These precompiled packages are designed to be portable, and +therefore omit support for parallel execution with MPI. Instructions +for installing LAMMPS--GUI and using its most relevant features for the +tutorials are provided in :ref:`using-lammps-gui-label`. + +.. |LAMMPSrelease| raw:: html + + release + +LAMMPS versions are generally backward compatible, meaning that older +input files typically work the same with newer versions of LAMMPS. +However, forward compatibility is not as strong, so input files written +for a newer version may not always work with older versions. As a +result, it is usually possible to follow this tutorial with more recent +releases of LAMMPS--GUI and LAMMPS; older versions may require some +(minor) adjustments. These tutorials will be periodically updated to +ensure compatibility and benefit from new features in the latest stable +version of LAMMPS. + +For some tutorials, external tools are required for plotting and +visualization, as the corresponding functionality in LAMMPS--GUI is +limited. Suitable tools for plotting include Python with +Pandas/Matplotlib :cite:`van1995python` :cite:`hunter2007Matplotlib`, XmGrace, +Gnuplot, Microsoft Excel, or LibreOffice Calc. For visualization, +suitable tools include |vmd| :cite:`humphrey1996vmd` and +|ovito| :cite:`stukowski2009visualization`. + +.. |ovito| raw:: html + + OVITO + +.. |vmd| raw:: html + + VMD + +About LAMMPS--GUI +================= + +LAMMPS--GUI is a graphical text editor, enhanced for editing LAMMPS +input files and linked to the LAMMPS library, allowing it to run LAMMPS +directly. The text editor functions similarly to other graphical +editors, such as Notepad or Gedit, but offers the following enhancements +specifically for LAMMPS: + +- Wizard dialogs to set up these tutorials +- Auto-completion of LAMMPS commands and options +- Context-sensitive online help +- Syntax highlighting for LAMMPS input files +- Syntax-aware line indentation +- Visualization using LAMMPS' built-in renderer +- Start and stop simulations via mouse or keyboard +- Monitoring of simulation progress +- Dynamic capture of LAMMPS output in a text window +- Automatic plotting of thermodynamic data during runs +- Capture of ``dump image`` outputs for animations +- Export of thermodynamic data for external plotting +- Inspection of binary restart files + +:ref:`using-lammps-gui-label` contains basic instructions for installation and using LAMMPS--GUI with +the tutorials presented here. A complete description of all LAMMPS--GUI +features can be found in the LAMMPS manual (see |lammpsguidocs|). + +.. |lammpsguidocs| raw:: html + + LAMMPS--GUI + +Content and citation +==================== + +All files and inputs required to follow the tutorials are available from a +dedicated GitHub organization account, |lammpstutorials_organization|. +If you find these tutorials useful, you can +cite *A Set of Tutorials for the LAMMPS Simulation Package* by Simon Gravelle, +Jacob R. Gissinger, and Axel Kohlmeyer (2025) :cite:`gravelle2025tutorials`. You +can access the full paper on |gravelle2025tutorials_arXiv|. + +.. |lammpstutorials_organization| raw:: html + + LAMMPStutorials + +.. |gravelle2025tutorials_arXiv| raw:: html + + arXiv diff --git a/docs/sphinx/source/non-tutorials/recommand-lj.rst b/docs/sphinx/source/non-tutorials/recommand-lj.rst deleted file mode 100644 index db2a2c236..000000000 --- a/docs/sphinx/source/non-tutorials/recommand-lj.rst +++ /dev/null @@ -1,4 +0,0 @@ -.. container:: justify - - If you are completely new to LAMMPS, I recommend that - you follow this tutorial on a simple :ref:`lennard-jones-label` first. diff --git a/docs/sphinx/source/non-tutorials/running-lammps.rst b/docs/sphinx/source/non-tutorials/running-lammps.rst new file mode 100644 index 000000000..4d4bafd59 --- /dev/null +++ b/docs/sphinx/source/non-tutorials/running-lammps.rst @@ -0,0 +1,73 @@ +.. _running-lammps-label: + +Running LAMMPS +============== + +From within the LAMMPS--GUI main window, LAMMPS can be started either from +the ``Run`` menu by selecting the ``Run LAMMPS from Editor Buffer`` entry, +using the keyboard shortcut Ctrl-Enter (Command-Enter on macOS), or by clicking the +green ``Run`` button in the status bar. While LAMMPS is running, a message on +the left side indicates that LAMMPS is active, along with the number of active threads. +On the right side, a progress bar is displayed, showing the estimated progress +of the current ``run`` or ``minimize`` command. + +Creating Snapshot Images +------------------------ + +Open the ``Image Viewer`` using either the ``Create Image`` option +from the ``Run`` menu, the ``Ctrl-I`` keyboard shortcut, +or click on the (right) palette button in the status bar. The image +can be saved using the ``Save As...`` option from the ``File`` menu. + +The Output Window +----------------- + +By default, when starting a run, the ``Output`` window opens to display the screen +output of the running LAMMPS calculation. The text in the Output window is +read-only and cannot be modified, but keyboard shortcuts for selecting and +copying all or part of the text can be used to transfer it to another program: +The keyboard shortcut ``Ctrl-S`` (or ``Command-S`` on ``macOS``) can +be used to save the Output buffer to a file. Additionally, the ``Select All`` +and ``Copy`` functions, along with a ``Save Log to File`` option, are available +through the context menu, which can be accessed by right-clicking within the text area of the +``Output`` window. + +The Charts Window +----------------- + +By default, when starting a run, a ``Charts`` window opens to display +a plot of the thermodynamic output from the LAMMPS calculation. From the ``File`` +menu in the top-left corner, you can save an image of the +currently displayed plot or export the data in various formats: +plain text columns (for use with plotting tools like Gnuplot or XmGrace), +CSV data (suitable for processing in Microsoft Excel, LibreOffice Calc, +or Python with Pandas), or YAML (which can be imported into Python using PyYAML or Pandas). +You can use the mouse to zoom in on the graph by holding the left button and dragging +to select an area. To zoom out, right-click anywhere on the graph. You can reset the view +by clicking the ``lens`` button located next to the data drop-down menu. + +Preferences +----------- + +The Preferences dialog allows customization of the behavior and appearance of +LAMMPS--GUI. Among other options: + +- In the ``General Settings`` tab, the ``Data update interval`` setting + allows you to define the time interval, in milliseconds, between data updates during + a LAMMPS run. By default, the data for the ``Charts`` and ``Output`` + windows is updated every 10 milliseconds. Set this to 100 milliseconds or more + if LAMMPS--GUI consumes too many resources during a run. The ``Charts update interval`` + controls the time interval between redrawing the plots in the ``Charts`` window, in milliseconds. +-The ``Accelerators`` tab enables you to select an accelerator package + for LAMMPS to use. Only settings supported by the LAMMPS library and local hardware + are available. The ``Number of threads`` field allows you to set the maximum + number of threads for accelerator packages that utilize threading. +- The ``Editor Settings`` tab allows you to adjust the settings of the editor + window. Select the ``Auto-save on Run and Quit`` option to automatically save changes + made to the ``.lmp`` file upon closing LAMMPS--GUI. + +See |LAMMPSGUIDOC| for a full list of options. + +.. |LAMMPSGUIDOC| raw:: html + + How-to LAMMPS--GUI \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/scope.rst b/docs/sphinx/source/non-tutorials/scope.rst new file mode 100644 index 000000000..9be136017 --- /dev/null +++ b/docs/sphinx/source/non-tutorials/scope.rst @@ -0,0 +1,66 @@ +.. _scope-label: + +Scope +***** + +This set of tutorials consists of seven tutorials arranged in order of +increasing difficulty. The novelties associated with each tutorial are +briefly described below. + +In :ref:`lennard-jones-label`, the structure of LAMMPS +input files is illustrated through the creation of a simple atomic +Lennard-Jones fluid system. Basic LAMMPS commands are used to set up +interactions between atoms, perform an energy minimization, and finally +run a simple MD simulation in the microcanonical (NVE) and canonical (NVT) +ensembles. + +In :ref:`carbon-nanotube-label`, a more complex system +is introduced in which atoms are connected by bonds: a small carbon +nanotube. The use of both classical and reactive force fields (here, +OPLS-AA :cite:`jorgensenDevelopmentTestingOPLS1996` and +AIREBO :cite:`stuart2000reactive`, respectively) is illustrated. An +external deformation is applied to the CNT, and its deformation is +measured. This tutorial also demonstrates the use of an external tool +to visualize breaking bonds, and show the possibility to import +LAMMPS-generated YAML log files into Python. + +In :ref:`all-atoms-label`, two components\textemdash liquid water +(flexible three-point model) and a polymer molecule\textemdash are merged and +equilibrated. A long-range solver is used to handle the electrostatic +interactions accurately, and the system is equilibrated in the +isothermal-isobaric (NPT) ensemble; then, a stretching force is applied +to the polymer. Through this relatively complex solvated polymer +system, the tutorial demonstrates how to use type labels to make +molecule files more generic and easier to manage :cite:`gissinger2024type`. + +In :ref:`sheared-confined-label`, an electrolyte is +confined between two walls, illustrating the specifics of simulating +systems with fluid-solid interfaces. With the rigid four-point +TIP4P/2005 :cite:`abascal2005general` water model, this tutorial uses a +more complex water model than :ref:`all-atoms-label`. A +non-equilibrium MD is performed by imposing shear on the fluid through +moving the walls, and the fluid velocity profile is extracted. + +In :ref:`reactive-silicon-dioxide-label`, the ReaxFF +reactive force field is used, specifically designed to simulate chemical +reactions by dynamically adjusting atomic interactions +:cite:`van2001reaxff`. ReaxFF includes charge equilibration (QEq), a +method that allows the partial charges of atoms to adjust according to +their local environment. + +In :ref:`gcmc-silica-label`, a Monte Carlo simulation in +the grand canonical ensemble is implemented to demonstrate how LAMMPS +can be used to simulate an open system that exchanges particles with a +reservoir. + +In :ref:`umbrella-sampling-label`, an advanced free +energy method called umbrella sampling is implemented. By calculating +an energy barrier, this tutorial describes a protocol +for addressing energy landscapes that are difficult to sample using +classical MD or MC methods. + +.. + In :ref:`bond-react-label`, a CNT embedded in + nylon-6,6 polymer melt is simulated. The + REACTER protocol is used to model the polymerization of nylon, and the formation + of water molecules is tracked over time~\cite{gissinger2020reacter}. \ No newline at end of file diff --git a/docs/sphinx/source/non-tutorials/using-lammps-gui.rst b/docs/sphinx/source/non-tutorials/using-lammps-gui.rst new file mode 100644 index 000000000..bcee45afb --- /dev/null +++ b/docs/sphinx/source/non-tutorials/using-lammps-gui.rst @@ -0,0 +1,157 @@ +.. _using-lammps-gui-label: + +Using LAMMPS--GUI +***************** + +.. admonition:: Note + :class: non-title-info + + For simplicity, these tutorials reference keyboard shortcuts + based on the assignments for Linux and Windows. macOS users should + use the ``Command`` key in place of the + ``Ctrl`` key when using keyboard shortcuts. + +Installation +============ + +Precompiled versions of LAMMPS--GUI are available for Linux, macOS, +and Windows on the LAMMPS GitHub Release +page. The Linux version is provided in two +formats: as compressed tar archive (.tar.gz) and as a |flatpak| +bundle. The macOS version is distributed as a +.dmg installer image, while the Windows version comes as an executable +installer package. + +.. |flatpak| raw:: html + + Flatpak + +Installing the Linux .tar.gz Package +------------------------------------ + +Download the archive (e.g., LAMMPS-Linux-x86_64-GUI-29Aug2024_update2.tar.gz) +and unpack it. This will create a folder named LAMMPS--GUI containing the +included commands, which can be launched directly using ``./lammps-gui`` or +``./lmp``, for example. Adding this folder to the PATH environment +variable will make these commands accessible from everywhere, without the +need for the ``./`` prefix. + +Installing the Linux Flatpak Bundle +----------------------------------- + +You have to have Flatpak support installed on Linux machine to be able +to use the Flatpak bundle. Download the bundle file +(e.g., LAMMPS-Linux-x86_64-GUI-29Aug2024_update2.flatpak) and then +install it using the following command: + +.. code-block:: bash + + flatpak install --user LAMMPS-Linux-x86_64-GUI-29Aug2024_update2.flatpak + +This will integrate LAMMPS--GUI into your desktop environment +(e.g., GNOME, KDE, XFCE) where it should appear in the ``Applications`` +menu under ``Science``. Additionally, the ``.lmp`` file extension will be +registered to launch LAMMPS--GUI when opening a file with this +extension in the desktop's file manager. + +You can also launch LAMMPS--GUI from the command-line using the following command: + +.. code-block:: bash + + flatpak run org.lammps.lammps-gui + +Similarly, for launching the LAMMPS command-line executable, use: + +.. code-block:: bash + + flatpak run --command=lmp org.lammps.lammps-gui -in in.lmp + +Installing the macOS Application Bundle +--------------------------------------- + +After downloading the macOS app bundle image file +(e.g., LAMMPS-macOS-multiarch-GUI-29Aug2024_update2.dmg), double-click +on it. In the dialog that opens drag the LAMMPS--GUI app bundle into +the Applications folder. To enable command-line access, follow the +instructions in the **README.txt** file. These macOS app-bundles contain +native executables for both, Intel and Apple CPUs. + +After installation, you can launch LAMMPS--GUI from the Applications +folder. Additionally, you can drag an input file onto the app or open +files with the ``.lmp`` extension. Note that the LAMMPS--GUI app bundle is +currently not cryptographically signed, so macOS may initially prevent +it from launching. If this happens, you need to adjust the settings in +the ``Security & Privacy`` system preferences dialog to allow access. + +Installing the Windows package +------------------------------ + +Download the LAMMPS--GUI installer for Windows +(e.g., LAMMPS-Win10-64bit-GUI-29Aug2024_update2.exe). Windows may warn +you that the file is from an unknown developer and was downloaded from +the internet. This happens because neither the installer nor the +LAMMPS--GUI application (or any other included applications) have been +cryptographically signed. You will need to choose to keep the file, and +when launching the installer, confirm that you want to run it despite +the warning. + +After installation, a new entry should appear in the Start menu. +Additionally, the ``.lmp`` file extension should be registered with +Windows File Explorer to open LAMMPS--GUI when opening a file with the +``.lmp`` extension. The ``lammps-gui`` and ``lmp`` commands should also +be available in the command-line. + +Opening, Editing, and Saving Files +---------------------------------- + +LAMMPS--GUI can be launched from the command-line, as explained above, where you +can either launch it without arguments or provide one file name as an argument. All +other arguments will be ignored. For example: + +.. code-block:: bash + + lammps-gui input.lmp + +Files can also be opened from the ``File`` menu. You can select a +file through a dialog and then open it. Additionally, a history of +the last five opened files is maintained, with entries to open them directly. +Finally, the ``Ctrl-O`` keyboard shortcut can also be used to open a file. + +When integrated into a desktop environment, it is also possible to open +files with a ``.lmp`` extension or use drag-and-drop. + +For the most part, the editor window behaves like other graphical +editors. You can enter, delete, or copy and paste text. When entering +text, a pop-up window will appear with possible completions after typing +the first two characters of the first word in a line. You can +navigate the highlighted options using the up and down arrow keys, and select a +completion by pressing the Enter key or using the mouse. You can also continue +typing, and the selection in the pop-up will be refined. For some +commands, there will be completion pop-ups for their +keywords or when a filename is expected, in which case, +the pop-up will list files in the current folder. + +As soon as LAMMPS--GUI recognizes a command, it applies syntax +highlighting according to built-in categories. This can help +detect typos, since those may cause LAMMPS--GUI not to +recognize the syntax and thus not apply or partially apply +the syntax highlighting. When you press the ``Tab`` key, the line will be +reformatted. Consistent formatting can improve the readability of +input files, especially long and complex ones. + +If the file in the editor has unsaved changes, the word +*modified* will appear in the window title. The current input +buffer can be saved by selecting ``Save`` or ``Save As...`` from the +``File`` menu. You can also click the ``Save`` icon on the left side +of the status bar, or use the ``Ctrl-S`` keyboard shortcut. + +.. admonition:: Note + :class: non-title-info + + When LAMMMPS--GUI opens a file, it will *switch* the working directory + to the folder that contains the input file. The same happens when saving to + a different folder than the current working directory. The current working + directory can be seen in the status bar at the bottom right. This is important + to note because LAMMPS input files often require additional files for reading and may + write output files (such as images, trajectory dumps, or averaged data files), + which are typically expected to be in the same folder as the input file. diff --git a/docs/sphinx/source/non-tutorials/accessfile.rst b/docs/sphinx/source/shared/access-the-files.rst similarity index 57% rename from docs/sphinx/source/non-tutorials/accessfile.rst rename to docs/sphinx/source/shared/access-the-files.rst index 226bcb0d7..baebde0d3 100644 --- a/docs/sphinx/source/non-tutorials/accessfile.rst +++ b/docs/sphinx/source/shared/access-the-files.rst @@ -1,9 +1,10 @@ -.. container:: justify +.. admonition:: Cite + :class: non-title-info You can access the input scripts and data files that - are used in these tutorials from |Github_repository_input_folder|. + are used in these tutorials from a dedicated |Github_repository_input_folder|. This repository also contains the full solutions to the exercises. .. |Github_repository_input_folder| raw:: html - this Github repository \ No newline at end of file + Github repository \ No newline at end of file diff --git a/docs/sphinx/source/shared/cite.rst b/docs/sphinx/source/shared/cite.rst new file mode 100644 index 000000000..226830809 --- /dev/null +++ b/docs/sphinx/source/shared/cite.rst @@ -0,0 +1,11 @@ +.. admonition:: Cite + :class: non-title-info + + If you find these tutorials useful, you can + cite *A Set of Tutorials for the LAMMPS Simulation Package* by Simon Gravelle, + Jacob R. Gissinger, and Axel Kohlmeyer (2025) :cite:`gravelle2025tutorials`. You + can access the full paper on |gravelle2025tutorials_arXiv|. + +.. |gravelle2025tutorials_arXiv| raw:: html + + arXiv diff --git a/docs/sphinx/source/shared/recommend-tutorial1.rst b/docs/sphinx/source/shared/recommend-tutorial1.rst new file mode 100644 index 000000000..5cb2d0aea --- /dev/null +++ b/docs/sphinx/source/shared/recommend-tutorial1.rst @@ -0,0 +1,2 @@ +If you are completely new to LAMMPS, we recommend that +you follow this tutorial on a simple :ref:`lennard-jones-label` first. \ No newline at end of file diff --git a/docs/sphinx/source/shared/versionLAMMPS.rst b/docs/sphinx/source/shared/versionLAMMPS.rst new file mode 100644 index 000000000..e5de8c377 --- /dev/null +++ b/docs/sphinx/source/shared/versionLAMMPS.rst @@ -0,0 +1,3 @@ +.. container:: version + + This tutorial is compatible with the 29Aug2024 (update 2) LAMMPS version. diff --git a/docs/avatars/level1/lennard-jones-fluid/avatar-LJ-LAMMPS-dark.png b/docs/sphinx/source/tutorial1/avatars/avatar-LJ-LAMMPS-dark.png similarity index 100% rename from docs/avatars/level1/lennard-jones-fluid/avatar-LJ-LAMMPS-dark.png rename to docs/sphinx/source/tutorial1/avatars/avatar-LJ-LAMMPS-dark.png diff --git a/docs/avatars/level1/lennard-jones-fluid/avatar-LJ-LAMMPS-light.png b/docs/sphinx/source/tutorial1/avatars/avatar-LJ-LAMMPS-light.png similarity index 100% rename from docs/avatars/level1/lennard-jones-fluid/avatar-LJ-LAMMPS-light.png rename to docs/sphinx/source/tutorial1/avatars/avatar-LJ-LAMMPS-light.png diff --git a/docs/avatars/level1/lennard-jones-fluid/avatar-LJ-LAMMPS.png b/docs/sphinx/source/tutorial1/avatars/avatar-LJ-LAMMPS.png similarity index 100% rename from docs/avatars/level1/lennard-jones-fluid/avatar-LJ-LAMMPS.png rename to docs/sphinx/source/tutorial1/avatars/avatar-LJ-LAMMPS.png diff --git a/docs/avatars/level1/lennard-jones-fluid/input.lammps b/docs/sphinx/source/tutorial1/avatars/input.lammps similarity index 100% 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index 100% rename from docs/sphinx/source/tutorials/figures/level1/lennard-jones-fluid/lennard-jones-fluid-avatar.png rename to docs/sphinx/source/tutorial1/avatars/lennard-jones-fluid-avatar.png diff --git a/docs/avatars/level1/lennard-jones-fluid/state.vmd b/docs/sphinx/source/tutorial1/avatars/state.vmd similarity index 100% rename from docs/avatars/level1/lennard-jones-fluid/state.vmd rename to docs/sphinx/source/tutorial1/avatars/state.vmd diff --git a/docs/avatars/level1/lennard-jones-fluid/view1.png b/docs/sphinx/source/tutorial1/avatars/view1.png similarity index 100% rename from docs/avatars/level1/lennard-jones-fluid/view1.png rename to docs/sphinx/source/tutorial1/avatars/view1.png diff --git a/docs/sphinx/source/tutorial1/exercises.rst b/docs/sphinx/source/tutorial1/exercises.rst new file mode 100644 index 000000000..e8521f470 --- /dev/null +++ b/docs/sphinx/source/tutorial1/exercises.rst @@ -0,0 +1,189 @@ +Going further with exercises +============================ + +Experiments +----------- + +Here are some suggestions for further experiments with this system that +may lead to additional insights into how different systems are configured +and how various features function: + +- Use a Nosé-Hoover thermostat (*fix nvt*) instead of a Langevin thermostat + (*fix nve* + *fix langevin*). +- Omit the energy minimization step before starting the MD simulation using either + the Nosé-Hoover or the Langevin thermostat. +- Apply a thermostat to only one type of atoms and observe the + temperature for each type separately. +- Append an NVE run (i.e., without any thermostat) and observe the energy levels. + +.. admonition:: If you are using LAMMPS-GUI + :class: gui + + An useful experiment is coloring the atoms in the *Slide Show* according + to an observable, such as their respective coordination numbers. To do this, + replace the *dump* and *dump_modify* commands with the following lines: + + .. code-block:: lammps + + variable coor12 atom (type==1)*(c_coor12)+(type==2)*-1 + dump viz all image 1000 myimage-*.ppm v_coor12 type & + shiny 0.1 box no 0.01 view 0 0 zoom 1.8 fsaa yes size 800 800 + dump_modify viz adiam 1 1 adiam 2 3 backcolor white & + amap -1 2 ca 0.0 4 min royalblue 0 turquoise 1 yellow max red + + Run LAMMPS again. Atoms of type 1 are now colored based on the value + of *c_coor12*, which is mapped continuously from turquoise to yellow + and red for atoms with the highest coordination. + In the definition of the variable *v_coor12*, atoms of type 2 are + all assigned a value of -1, and will therefore always be colored their default blue. + +Solve Lost atoms error +---------------------- + +For this exercise, the following input script is provided: + +.. code-block:: lammps + + units lj + dimension 3 + atom_style atomic + pair_style lj/cut 2.5 + boundary p p p + + region simulation_box block -20 20 -20 20 -20 20 + create_box 1 simulation_box + create_atoms 1 random 1000 341841 simulation_box + + mass 1 1 + pair_coeff 1 1 1.0 1.0 + + dump mydmp all atom 100 dump.lammpstrj + thermo 100 + thermo_style custom step temp pe ke etotal press + + fix mynve all nve + fix mylgv all langevin 1.0 1.0 0.1 1530917 + timestep 0.005 + + run 10000 + +As it is, this input returns one of the most common +error that you will encounter using LAMMPS: + +.. code-block:: bash + + ERROR: Lost atoms: original 1000 current 984 + +The goal of this exercise is to fix the *Lost atoms* error without +using any other command than the ones already present. You can +only play with the values of the parameters and/or replicate every +command as many times as needed. + +.. admonition:: Note + :class: info + + This script is failing because particles are created randomly in space, some + of them are likely overlapping, and no energy minimization is performed prior + to start the molecular dynamics simulation. + +Create a demixed dense phase +---------------------------- + +Starting from one of the *input* created in this tutorial, fine-tune the +parameters such as particle numbers and interaction to create a simulation +with the following properties: + +- the density in particles must be high, +- both particles of type 1 and 2 must have the same size, +- particles of type 1 and 2 must demix. + +.. figure:: figures/demixing-light.png + :alt: VMD/LAMMPS exercise molecular dynamics simulation: demixing lennard + jones fluids + :class: only-light + +.. figure:: figures/demixing-dark.png + :alt: VMD/LAMMPS exercise molecular dynamics simulation: demixing lennard + jones fluids + :class: only-dark + +.. container:: figurelegend + + Figure: Snapshots taken at different times showing the particles of type 1 + and type 2 progressively demixing and forming large demixed areas. + +.. admonition:: Hint + :class: info + + An easy way to create a dense phase is to allow the box dimensions to relax + until the vacuum disappears. You can do that by replacing the *fix nve* with *fix nph*. + +From atoms to molecules +----------------------- + +Add a bond between particles of *type 2* to create dumbbell molecules instead +of single particles. + +.. figure:: figures/dumbell-dark.png + :alt: Dumbbell Lennard-Jones molecules simulated using LAMMPS + :class: only-dark + +.. figure:: figures/dumbell-light.png + :alt: Dumbbell Lennard-Jones molecules simulated using LAMMPS + :class: only-light + +.. container:: figurelegend + + Figure: Dumbbell molecules made of 2 large spheres mixed with smaller + particles (small spheres). See the corresponding |dumbell_video|. + +.. |dumbell_video| raw:: html + + video + +Similarly to the dumbbell molecules, create a small polymer, +i.e. a long chain of particles linked by bonds and angles. + +.. figure:: figures/polymer-dark.png + :alt: Polymer Lennard-Jones molecules simulated using LAMMPS + :class: only-dark + +.. figure:: figures/polymer-light.png + :alt: Polymer Lennard-Jones molecules simulated using LAMMPS + :class: only-light + +.. container:: figurelegend + + Figure: A single small polymer molecule made of 9 large spheres mixed with + smaller particles. See the corresponding |polymer_video|. + +.. |polymer_video| raw:: html + + video + +.. admonition:: Hints + :class: info + + Use a *molecule template* to easily insert as many atoms connected + by bonds (i.e. molecules) as you want. A molecule template typically + begins as follows: + + .. code-block:: lammps + + 2 atoms + 1 bonds + + Coords + + 1 0.5 0 0 + 2 -0.5 0 0 + + (...) + + A bond section also needs to be added, see this + |molecule_template_lammps| for details on the formatting of a + molecule template. + +.. |molecule_template_lammps| raw:: html + + page diff --git a/docs/sphinx/source/tutorial1/figures/LJ-energy-dm.png b/docs/sphinx/source/tutorial1/figures/LJ-energy-dm.png new file mode 100644 index 000000000..fbba0fa2d Binary files /dev/null and b/docs/sphinx/source/tutorial1/figures/LJ-energy-dm.png differ diff --git a/docs/sphinx/source/tutorial1/figures/LJ-energy.png b/docs/sphinx/source/tutorial1/figures/LJ-energy.png new file mode 100644 index 000000000..1c4cebfa5 Binary files /dev/null and b/docs/sphinx/source/tutorial1/figures/LJ-energy.png differ diff --git a/docs/sphinx/source/tutorial1/figures/LJ-mixing-dm.png b/docs/sphinx/source/tutorial1/figures/LJ-mixing-dm.png new file mode 100644 index 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b/docs/sphinx/source/tutorial1/figures/improved.md.ipynb new file mode 100644 index 000000000..37687261a --- /dev/null +++ b/docs/sphinx/source/tutorial1/figures/improved.md.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c0471c3", + "metadata": {}, + "outputs": [], + "source": [ + "def sliding_average(data, window_size):\n", + " \"\"\"Calculate the sliding (moving) average of a dataset with edge handling.\"\"\"\n", + " pad_width = window_size // 2\n", + " padded_data = np.pad(data, pad_width, mode='edge')\n", + " smoothed_data = np.convolve(padded_data, np.ones(window_size) / window_size, mode='valid')\n", + " return smoothed_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6156a6b", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"improved.md.log\")\n", + "timestep = 0.005\n", + "time = log.get(\"Step\")*timestep\n", + "population1 = log.get(\"v_n1_in\")\n", + "population2 = log.get(\"v_n2_in\")\n", + "coordination = log.get(\"c_sumcoor12\") " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7f303b27", + "metadata": {}, + "outputs": [], + "source": [ + "smoothed_time = sliding_average(time, 5)\n", + "smoothed_coordination = sliding_average(coordination, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "b1a7c6ae", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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ERInT19fnd3n9+vWJWQgRERER0RTDYC8MzzzzzPjXZWVlKC0tFe4X6QyBwsJCv8s2my2u59fS3Nwc0v7FxcUoLi4WXrdr1y6/UK+srAwHDx4M6fxERERERNNVW1sb2tradO8f6t/yRKEK7DCj1OmGiIiIiIhCw2AvRPX19X5z3w4cOKC4b+AnFEMVWFEXGOTF+vxaqqqqQtr/sccew9e+9rWg7VarNaitptrPlYiIiIiI/B0+fBhPP/10opdBNK6pqWn8a7PZHPLoCCIiIiIiEjMmegGTzZNPPjn+9d69e1XbRPoGgED0W1/G+vzxEhjqVVRUsP0mERERERHRJHb8+PHxrysrKxO4EiIiIiKiqYXBXgiqq6vH20Vu374dO3bsCOn4SFtnagV3sT5/rASuW++swEi/XyIiIiIiIoq++vr68dfOZrM55NfORERERESkjK04dbJYLKipqQEgz3/bt29fzG8zMODSG3jF6/zV1dVYtWqV7v2V5usFBoqBsxhEdu/e7TeTj4iIiIhoOtu2bRvKysp079/c3Bxya30ivbydbkwmEw4dOpTg1RARERERTS0M9nSw2+148MEHAQClpaU4fPhwXG7XZrMl9flXrVqFDRs2RLyOdevW+V1ubGyE1WoVzmCw2+14/PHHUVBQALPZzHCPiIiIiAjyh+iUPkhHFI7A0Q/19fXYsmWL5nHeTjcmkwnPP/88Z+sREREREUUZW3Hq8OCDD8Jut8NsNuP555/XfVzgnLhIg7TAyrZYnz9eSkpKgrY99NBDQaFdbW0t7r//fpSXlwsrJgNfeBIREREREVF07Nq1S7Nzyu7du1FTU4OysjKcOHECpaWlcVwhEREREdH0wIo9DTt37oTFYoHJZMLRo0eDwjQ1hYWFfpdDnQkXuP/69evjev54MZlMqKqqQnV19fg2q9WKjRs3jr8Q9N4Hzz///Pi2wCDzwQcfxNatW5Gfn88ZDkRERERERGFS+tBkXV0d6urqUFpairVr12LRokUAgJaWFtTV1QEAqqqqUFlZGa+lEhERERFNOwz2VOzevRtHjhwJK9QDgivgWlpaQjq+r6/P73JgZVuszx9PlZWVaGhoQGNjo992i8UCQJ5reODAAb/7IPDFZmtrK5555hnY7XY88MADId9fRERERERENPEhSpPJhEceeWT8dZaXxWIZf63mZTabceDAAVbpERERERHFGIM9BdXV1airqxsP9cKZCxA4Oy7UVpmBFXWBa4j1+ePt8OHDqK+vR21tLVpbW8fnMmzfvh1VVVVB+7e1tSVglURERERERFObw+GA2WzGoUOHYDabUVlZifr6evzqV79Ca2srWltbYbfbYTKZUFJSgh07drBrChERERFRnDDYE6itrUVNTQ0ARDTsO7AC7vTp0yEd39ra6nc58JOPsT5/ImzZskXXQHYiIiIiIiKKjdLSUrz22mt+2/hajYiIiIgoORgTvYBkU1tbiz179gAADh06FFHYZTKZ/NpBqg0ZF/FtrVlWVhb38xMREREREREREREREVHyYLDno76+3i/UKy8vj/icmzZt8rscOIdATVNT0/jXSmuJ9fmJiIiIiIiIiIiIiIgoOTDYu6mhoQG7du0CABw4cCBqQdfWrVv9Ljc2Nuo+1jek2759e0LOT0RERERERERERERERMmBwR7kgOuhhx4CAOzduzekuQF2ux1WqxVWq1VYLRd4rtraWl3nra+vH/+6oqLCr+VmPM9PREREREREREREREREySE10QtINKvVigcffHD8cm1tLWpra+FwOMa32Wy28a/tdrviuUwmE5qbm4O2V1VVobq6evz2GhoaNCsCn3nmGb/j1cT6/ERERERERERERERERJR407piz2q14v777/cL6ywWCywWy3gVntVqhd1uH/8XjsrKSpjN5vHL3/jGN1TPVVtbO179t3fvXr9jE3F+IiIiIiIiIiIiIiIiSrxpG+zZ7fagUC+Wjh49Ot7u0hsoilp31tTUYM+ePQDkwG7Hjh1JcX4iIiIiIiIiIiIiIiJKrGnbirO1tTWkUE9pBp3vObTadJ44cQKPP/44jhw5AqvVis2bN8NsNmPNmjVwOBxoamoaP8eBAwdCmvUX6/MTERERERFR8nn44YeRnp4etH3nzp3YtWtXAlZERERERES+Dhw4gIMHDwZtHxsbC+t80zbYKy0tRVtbW1xv02Qy4eDBg7BYLPjRj36E48ePw2az4ciRIzCbzVi7di22bNkSdhVdrM9PREREREREyaW3t1e4fWBgIM4rISIiIiIikYGBAdy4cSNq55u2wV4ilZaWYt++fZP2/ERERERERJQcioqKhBV7ubm5CVgNEREREREFys3Nxdy5c4O2j42NKX5QTw2DPSIiIiIiIqJJ6vvf/z42bNiQ6GUQEREREZGCXbt2Cdvkv/nmm/jMZz4T8vmM0VgUERERERGFSPIAQx2Ax53olRARERERERHRJMGKPSIiIiKieOu2AG9+Gxi1ARmFwAf+Cph7Z6JXRURERERERERJjhV7RERERETx5HEBbzwph3oAMNonh3xDnQldFhERERERERElPwZ7RERERETx1Ps+MGb33+YeAc4djv9abrwJNH5D/nf99fjfPhERERERERGFhMEeEREREVE8KVXmtRwDRvrit47Od4ATfw90N8n/Xv9H4Pob8bt9IiIiIiIiIgoZgz0iIiIionga7lG+7uLPwz9v71ng/E+BtuOAJGnvf/GXwduuHAn/9omIiIiIiIgo5lITvQAiIiIiomllRCXYu3IEWP45ID0vtHNe/Dlg+feJy/M3And9S/2YjrcE206GdrtEREREREREFFes2CMiIiKi5DfYIbep1FOJluyGu5Wvcw0DbY2hnW9sAHi/1n9b+2tyBZ/aMUo87tBuf7oZvAGM2rX3IyIiIiIiIooBVuwRERERUfJyjQBvPjlRSbbgPuC2vwRSMxO7rkioVewBgMMa2vk6TsqBoGh70UrxMf1Xlc830g1kzwltDdPBqB14ay/Q9Z58efEngbW7AGNaQpdFRERERERE0wsr9oiIiIgoeZ095N8e8torgPXXiVtPNKjN2AOA0b7Qztf7vnh753vKx6gFe0Odod3+dHG2biLUA4ArL8rzDImIiIiIiIjiiMEeERERESUnjxu48JPg7ddfj/9aosXjlluKqgm1zWPfOfH2FJVKMtVgryu0258OJDfQ+rvg7e0n4r8WIiIiIiIimtYY7BERERFRcuqxiLd3nYrvOqJptA+AR32fEZv+87mGAdtF8XWDN5SP629Vvm6YFXtB7C2Aayh4+0hv3JdCRERERERE0xuDPSIiIiJKTu2vJXoF0Tfcrb3PqE3/+XrfBySFoHCoC/A4xdcNsBVnSLoVQuZQ7isiIiIiIiKiKGCwR0RERETJR/Kotzn0uOK3lmjSE+w5+/V/f91nVK70AIMdwZvdo+LtXgz2gvWcFm9nsEdERERERERxxmCPiIiIiJJP33lgpEf5+sA5dJIEOKzqgVUyUPuefOmds6cUOHkNXg/eNtAGQFI+hsGeP0lSDlBdQ4B7LL7rISIiIiIiomktNdELICIiIiIK0vaq+vWjNiBrhvz1SB/w+j8BfWflyws/Atz2KJCSHtMlhmVYb7Bnm/j+lLidQO859X1Ec/b6VdpwAsBwlxxmGQzq+00XA9eAMZWgddQGZM+O23KIiIiIiIhoemPFHhEREREln+sa8/V8WyC+/6OJUA8Arr4MtDXGZFkR09OKE9DX4tF2HvBoVIuJKva0gj33KDDm0L796UK13SnYjpOIiIiIiIjiihV7RERERJRcxvrFgZQvb5jicQMtx4Kvbz8BlHwktNt1jwLN/w3cOAmk5wKrvgjMWhvaObTobsVp097Hfll7n8H24G39rdrHDXUAGSbt/aaDHov69Qz2iIiIiIiIKI5YsUdEREREyUUr1AMmZtDZL4mv16r4E7H8B3Dx58DAVaD3feD4E8C1V0I/jxrdrTh1zNgb0PFzCqdiD+CcPV/dGnMMGewRERERERFRHLFij4iIiKaukT6g5SjgHAIWlAOFyxO9ItJDV7DXJ/9XK3TRy+MEWl8O3Aic3A8Y04D5GyO/DUkKoRVnn/9xnW8DHSeBrNnA4k8CqZni+XmBBm8Akgcw3Pw8n+QBBtq0jzu5T/6ZLPjg9J61N9QhzxxUM2KLy1KIiIiIiIiIAAZ7RERENFWN9AK/e3QiILn4C+CevwPm3pnQZZEOA4L2kYG8FW09KvPPPC7AqPPP3eFuwD0SvF3yAG8+BXzwaaBgmb5zKXEOaM/E8/Kt2Lv0C8Dy7xOXr/4O+OC/idtsBvI45edC1kz58kiv/HPRPM4lh5rdp4HbHtW35qlIa74ewIo9IiIiIiIiiiu24iQiIqKp6XK9f9UTPMC7/w9wOxO2JNJJb8We5FEP9kb6lK8LpNZ6UnIBF/5X/7mU6K3WAyYeu8PdwOn/9L/OfhloO66vYg+Qf56DN4C+84DDqn8NANDy6+ndlrNHR0XomB1o/i/g/Trg6u+B3rNyMCtJsV8fERERERERTTus2CMiIqKpqdsSvG2kR263uHhz/NdD+umdsdd/DRhzKO8z3A1kz9J3m1rhlZ7qOC0hBXs3K/Yu/EwOFgNda9Bf/ff6PwKuYTkIDZlHDgSzZ4dx7BSgp2JvpA+4/rr8M/a18EPAHY/HZl1EREREREQ0bTHYIyIioqlHrZLrwv8A5o8BxpT4ron001OJNmoDegThra+RHv23qTVHbaxf/7mUKK0na2Zw6DfSJ3+PLcfEx9x4Xf/tOgf17yuiJ2idikZtwMBV7f36W4NDPWCi/SkRERERJcyNgREcfPcyrjqGsXa2CV8oLUFBZnqil0VEFBEGe0RERDT1qAURgzeAtga5mobC1/4a0Pzf8ly6eRuB1X8KpKRFfl7XiL5AbtQmz39TE0qFnFbF3tiA/nMpGVb4vkxLg9c6agcu/Bxwj0Z+u5GarsGenmo9QJ5bKJIzL3prISIiIqKQOd0ePPbbJnQNyX9Tt9iHcK1/GE99aA0MBkOCV0dEFD4Ge0RERDT12C6pX3/+f4AFHwT4Yi48jlbgjWoAN2eIXfqFXAG55uHIzz2kc26cxwXceFN9H6UgTXi7GsGec0CuBDVEMKJ6VDDzz2AE8s3AjTf8t0su4MJPQr+NoluB3vfDW5+S6Rrs6Zmvp4bB3rRjt9vxwgsvoKCgAPn5+SgsLPS7Pj8/f/xrh2OijXBfX9/45dLSUpjN5vgsmIiIaIp7ra1nPNTzeueGDU2ddqybU5CYRRERRQGDPSIiIpp67BrBnsMqB0h84z08l1/AeKjn1fq76AR7AyGESKL2h75GugHJDVz9A9B7Vr6/l1QAKRnB+2oFe5DklpbpefrXF7QeQWVXRgGQWRT+OX2lZAJ37AZeeUwcIiopuhWY/QHg3CHxHL6uU0DTc4AxDVi8BciZE531JjutilAt/P0y7Zw6dQp79uyJ6BxVVVWorKyM0oqIiIimtzNd4nncPz3bxmCPiCa1CD5yTERERJSktCr2ALnqjMJz5UjwttE+uY1mpKJZHTbcDTQdAN7+V3nNp/8DePVvgsMryQMMawV7kKv2IiEK9jKLgAxTZOf1ypkrh26bngTS87X398qeDdy6Hbi/Dpi1TrzPpV8BF34G/K4S6L8WnfUmM+cgYL8iuEJnla8xFciaEdUlEREREVFozvWI/35/+3of7CPOOK+GiCh6GOwRERHR1CJJ+oK9fpVgb7gb6LYAQx3RW1csuJ1AV5M8C0yStPePBo9b+TqlWWOhiGaw13MGuBwQQvacDm7hOWqTW3tqGevXf9sjfUDXe8BAm/+2QBmF8r9o8FaI5ZuBe6v1nzdr1s21mIDZt6vv6xqWW68mq/5rcoXhqC2y83SfBiCoXpyxSt/x2fMAQ0pkayAiIiKisHkkCS32QeF1To+E37bo+GAfEVGSYitOIiIimlpGeoAxu/Z+/VfF21tfBt57BnCPyq0HS/8cWPJAdNcYDf1Xgdf/GRi4+X0UrgA2fRtIzYzt7Q4o/NwAObjKnR/Z+Qd1ztjTTRB4XmsA5t09cVmzDedNeiv2rL8Fmr4nh2AGI7ByO7Bim7g9Zmah3I4zGnJ8fvYFS4GPHZQfz03PqR+XPdvnHDraR7b8Blj/SHhrjKXT/wlc+CkACUjLBe7cA8zRCCqVtL8m3l68SQ6MteTMDe92aVLznalXWVkJu13+f5HNZhvf7jtbL/A6ACgpKYnZ+oiIiKaT9v4RDDmVP5R45OJ1/NGK+TBw7joRTUIM9oiIiGhq0VOtB4gr9lwjcgjivjlg3eMETv8AmH9v9OagRcNAO3D8m/4Vcn3ngDM/BNZ9Oba3rfbzHY1GxV578LY8M9BvjfzcXtf+ANz+13JwC+gP9vRU7Fl/C7zz9MRlyQO8XwfMWiuuCoxmK87cgDApLQdYuhUY7gEu/ET5ON9gL1dHsCe55NmFeirSJAmIx5slPc3+36NzADj1PTncNITYpMTjBq6/Hrw9c4Y8i1APPT9HmrJMJhOqqqoSvQwiIqJp7UKv+t/uVx3DaO7ux+pZIbSwJyJKEmzFSURERFOLXW+wdzW4fWXPGXm2li/3qFz1lCyGuoJDPa+Wo9Fph6lGLdiL9LY9bnHIVrg89HBGS5dl4mvdwZ5Gxd61BuCdfxNc4QluCeqVWQSk52l/f+aPa69Pqdoub6H6cb7BXrbOSrPBTvnn9vo/Ai/8MfDyV262r7ypqwn47ZeBX31G3mdURxVtJC7+XLDGdv3zAD1u4P1a4Mg24JcPAE7BG0Hz79Hf3lRP5SNNWQUFBYleAhER0bR3oU+724alM8Z/oxIRxQiDPSIiIppa9FbsuYbltp2+7FfE+7a/Gtmaoum9Z4DhLvF1Hqc44Igm+2Xl60Qz5EIx3ClXggXKnQ+kR6mqzcv3Ph2OQivO4W7gne9AOJcNANoaxNszCuVQT+37K1wJ3PLH2usLN9jL8gn20rL1hVf9rfL3e/11wDUEOKzAib+Tw8+RXuDVb8n7eMbkfd7459jNgZTcys9RvTMbrceAsz8GxhzK+8zfKFdBGnU0PWGwR0RERJRQF3q1g70W+1AcVkJEFH0M9oiIiGhqcYTQsjFozp5C8BBpYBWJkV7g9PeBk/uB8z8BOt5S3//Ki/paRoZDktQrIiOt2Os7L96eMzd6c+i8rp+YCBGHFILSQGo/1xsnJ1q4ikgKgZ+3xavS95eeD9z+mBxupmYpn9+Q4h/Q+VIL9tJy5TDPl575cO2vAl2n/Le5hoG2xps/24C2oz1ngBtvap83HD3NytfpDfYu1atfn5YHzCiV24rqeSwy2JvW8vPZ0ouIiCiRPJKkK9iz2gc19yEiSkYM9oiIiGhqEVXc5BaL9w0M9pQCvJE+5WAmlsYGgN//JXDhZ8DV3wNn/lP7GNcwcOmXsVnP0I3gVqW+RiMMQNtPiLfnLwYyCyI7d6BRG9Dzvvy13lacahV74c4AzLxZHTezNPi6tFzg3mogb4Fc1TdzrfJ5ilYCRoWZd2nZ8nw4kWxBGKgnlGr9rXh7WyMwcEN83ZkfxqZqr/015esGFdbia9Suff/Nu3vi56sZ7Bn0tzSlKYmtOImIiBKrvX8Eg05BJ5AAVvsQ3J4YdZUgIoohBntERESTVf81OQjR88b1dOFxi8OXGavF+wcFewoVZ5JLf+VPNF39fXC7UD1ajsUmiNRqc6qnsnGkF2g7HvzzdI+JqxFz5ssVZ1phSv4iwKCjRaIv7+3pbcWpVrEXVP2pk7dib+kDQNasie0ZhcDGfwQKlk5sW/knctgXKCUTuPUL6rejVLUnqvKLpNpsrB8YVXge9VuB7lPi67xGHfLjo/esvtuTJI1gr13+r8cNdL0HdLwtt6z11XNG+3aK7534OkOjLWzWTCAlTfucNOX09SWwupuIiIjGXejV18HE6ZFwfWA4xqshIoq+EN/9ICIioqRw8edye0bJI7fgW7sTWPJAoleVeEoVVdmz5dAkcDZdYBijVnFmu6hc+RcrtgvhHTfSKwcjM1ZFdz1qbTi9t6vmWgPw1l6Mtzxd9UVgxYPy153vytWGgeZvlNsfZs9RP3fBMvn4oQ71/XwNXpcrENWqEH2pVuyFEeyl5QIp6fLXucXAh5+RQyrJAyy4L7hFZuEt8j7X35j4WWeYgLkb5FadanLmAqKOo2k5wdu0zqVm1AaMFSlff+55YNZ68XVdTfKcPm9L0/n3And8HUjJUD6f7YLyzElAvo9H7cCJfwD6boaFeWbg3n+SAzgA6D6tfDwgt0D1XbNWNR7bcE57bMVJRESUWBf79LfYbLEPYUF+tvaONOk43R7UnWlFc1c/5udl4vO3LsD8PJXxBkSTCIM9CtvDDz+M9PT0oO07d+7Erl27ErAiIpoSvG3aDIbEriOZjfTdbGl3syJLcgOW7wNz7wayZ6keOk7yyK39phpRG04ASM+TK5a0gj21YMp2WQ5b4qn3nPY+c+4AOk4Gb29/LfrBnu2i+vWjdrkyStQScrgbePtf4TfHsPm/gJlr5IrK9lfF55y/Uf7vwg/JLUkDq628CpYCA+2hBXvDPfrbcAJyNZrkAWDw/x3lHFIPl5RkFPpfTs8DFn1C/Zjs2XJ1X6hSMsXbA2fhAZEFUyM96rMAu04BjlYgv8R/u3tUDn195xS2vwq8MQbc/S3AeLMCLvDxdfUP6usZ7JDnTvb5VAD2W+XH0tqbf69qVewtf3AigAXk6r0rR5T3T7Jg78CBAzh48GDQ9rGxsQSsZnrwbcVptVpRW1uLxsZGtLa2wm63w2QyoaSkBFu3bkVFRQXMZnPiFktERDQFtTqGdO/bYhvCJpWR1DR5/cvr5/E7q/w67d0O4PctXajatBIb5qt8EJFokpiC7+hRvPT29uLGjRtB/wYGtIfTEhEFGWgHGh4HfrkV+O2X5eqdWOu/Chz/JvCLrcBv/lyuFpkMuk4FhxueMeDqy+rHuUaAd/4v8Ks/Ao48BFz439itMVEUg718cSvC0T7/9opqwZ5WtVosuHR80nTdV8RBSvtr0Z1n5hoBuiwaO0lyxZbIhZ+KQ7lzhwGPS65CC5Q5AyhcLn+dtxC443Eo/vlqWgrkKFT1mZYChSuDtw93K7eyNQjCSUeL/Pui4euA/crE9oFr4nNoySzU3ida5t0l3j5/U/C2SIMprZ/Hib8FXvgs8MtPy5XHHhfQ8htxxWzHW8Cbe+X/J/zmz4FfPgC88jU5HBy1y21n1Ugu4P0fBW/3zqF0Dim3mJ2xBlhXCdzyWf/tM9dNPC5FkizYGxgYEP7N3turUWFLEbFardi2bRs2btyImpoaWCwW2O12AIDdbofFYkF1dTU2btyI3bt3J3i1REREU0tbf3AnkBVFuUgzBn+AuMWuv7qPJg/byBj+0Or/4cshlxt/88oZ/PJ8e4JWRRQ9DPYobEVFRZg7d27Qv9xcwewXIiI1kgS89ZRcNSG5gYGrwJvf1jevK1wOqxwkdp26OT+tHTjx9/pb8iWSUtXUlaPqx507DFh/DbhHAGc/cPo/gBuCmWaTmdIMNG/Fnkh/q/xf14i4FaSX7VJ0gzItrhHt1paFK+UQYe6G4OuGbgD2y9FbT8dJOUDWIlrzqA1o+bXCed+WK/mcgvtu/kb/ytLiTcDtjwEIeEGeWwwU3QoUlwefI88M3PvPQL6gImakd2IGW6CCW8Tb4QF635d/Z3mDykjn68XDjDVyO05fabnAnNuD980wAbMF26NlqFN+rnnG5Mq5y/Vy8Kvk+mvAq1UT91Xv+8Dr/ygf4x4Jfx0el3wuCOZRlu4EyvcBS7YEV6AaDMCKbcrnjaSVaQzk5uYK/2YvKuInlaPN4ZA/XFJfX4+NGzeisbFR13F1dXXYvHnzePBHRERE4XN7JLT3B/+NWGLKRokpuOWm1a6/uo8mj6uOYXgEL989EvDMyUs41WGL+5qIoomtOCls3//+97Fhg+CNRCKiUPW3BodVzgGg9WVg+R9H//YG2oDjTwRXd7lHgPYTgPmj0b/NaFJqHTjcJQcVSmHB1d8Hb7v4c2DundFbW6KpVex5W/kFav2d3ApSK0geswN95+THpXNQroAqLo9O21i3E7j0i5tz/BbIj3ulSjJf3jaV8+8Frr0SfH37a3KLynA5h+R12VuA9uP6jhFVXV38uX+LxUCitQMT35+vkg/LM9eanpPbPuYulMM+Ywow9w5g5Xbg3CG5Zeb8TXJFY4YJyJoRfC7JLc8iFClc5t++MVD/VaDjHflx4A2HQxXPij1jCnD748A735Er6jJnALf/NZCq0KJzXSVwcp/8mDekyD+rWLEEt4nUNNguh4KRGOoCehTm681YrX7s3A1AWp44kE6yir1du3YJ2+S/+eab+MxnPpOAFU193oCutLQUW7duRUlJCfLz8+FwONDQ0ID6+vqgEM9isWDXrl04fPhwyLfX3Nwc0v7FxcUoLo7zzFgiIqI4uT4wArfgA5kL8rPgkYBLAfP3rjqG4XR7kJbC+peppHNQ5fUngFdau7FuTkF8FkPTSltbG9ra2nTvH+rf8l4M9oiIKPEcLeLtZ34Q/WDPNQK89nfi8AFQrt5JJmrVQddPAIsrgre7neIZYF3vyVVu6XlRW15CqVXs5cwHjKlylY6v1peAlQ8Bozra0r3y2MTX1/4gz2i75Y/CXi4AOSR8tQroOz+xrbsJWKbxhrshRa5gA+Q5e8b04Iq69leBVV8Ib12SBLxZHXpb3MCKvf5rckVWqDJnyFVmIsX3AvM2yI/r1IyJtpmGFODW7cCKz8vXpfl8Ijdzpvhc3YJgJ3OG/E/L5fqbwZ7gOZmaBWTPUf79BsS3Yg8AZtwKfOR78uPEmC6eheiVOw+472k5kDUY5DaYIz3xW2s8DF4X3/+pWYBpifqxBiOwvlKeC+gr3QTklYiPoSnPZrONf713717s2LEjaJ8tW7Zg3759qK6uRk1Njd91jY2NqK2tFR6npqqqKqT9H3vsMXzta18L6RgiIqLJQtSGEwAW5GXBKPhQpluS8Nb1PtxTXARDND60SUmhY0i9s0eXRvBHFK7Dhw/j6aefjvnt8KMIRESUeGpB1XB3dG+r42318E6pGk5EkoDOd+RqI62WiYBcGXT193LFoC+PC7jxJnCtQQ4e1Xjcwcf7eu9ZoO14cEtJtZ+jaLbZZKVWsZeWDSz4UPB1Hpc8bzCc1q8X/zey9pyuYeC1v/UP9QC5La1S60qvlQ9NtFZMzRS3VOxvBbrPhLe2rlPhzbr0fS4M3gBefUK9xamStbvUgydjmnyfimbhea/zJarYA+RKzEA584B0Ha3F+87Jz0nR77C8heKKQ1/xDvYA+WeamqX+s/UyGOTHVkoGkD8Fw6qBq8HPPQAoWqXv51NcDiz0+Z1iMAJr/hRIUagOpmnBZDLh2LFjmuFcVVWVMJB78sknY7U0IiKiaeGaYrCXjUWCVpwA8HcNzfirl06hb0TH6AGaFLQq9myjgvnvRJMIK/aIiCjxtCrQljwQvdvSmjkmqmoTkTzyTL6Ok/Ll1CzgA389UUEVqOkAcOmX8teGFGD9o8CijwNjA8Br35p4czmzCNj0FJC3QHyewevyTEA1bz4pzxwr3w9kFNz8vjqU929/Lfnbj+olqtgzpgIpN9sNLv+c3EozcKZWy9HgIEiPkV65+jOcgEaSgDefujnjS6BDYf5h6U6gaKX8z9f8jfLzJdD5w8DMfwp9fe2vhn4MMBGQukbkUC+ccL50p/JzKVxZChV7Ijnz5DaLWpwDQNe78vMyUF6JfJ+c/bHy8RlxbMUZKdOy8ILeeJq1Xv4dYL+kb//rb0zMSfQ1Y5W+4w0G4PavAeZPyB+4KFoJmBbrXi5NPTt27Aip2q6yshK1tbWwWq3j2+x2O6xWK8xmwVxQIiIiUmQfceKSbQCvXhO//ijOy0R2mvKHt5q7+/FfTVb81QalWds0mWgFew4GezTJMdgjIqLEUwv22l+LbrCndluAf8Wed6aUqCLo6u8nQj1Arkh6a6+87/x75G0el3wO+5WJUM97XssBeb+WX/tXjIz0As0/BO76Vnjr9xpoA84eBtZ9Ofj7CtT5tjxLLZxgK9mIKvbS8yfm4OUtkAOjtgb/fdyj8hy4cAxcDy/Y63pXObxTkjUTWPZp8XXzNwJNB4NnfnW8DfRdAApDeIEqeeTnnpqMQgASMGrz3+4N9i79Ut+cwECrvqj8PUZCT2tNr1ydFXsAcP6n8s8rUN5CIH+xXFWp9HNIRMVeuBZ9HLj8K/VZidGSni/PmuwNYdaAwQis/hJw4af6g72u98TbQ3muGIzArLXyP6IwPPXUU3jooYf8tjU2NoYU7FVXV2PVKp2BNMD5ekRENOWc6rDhH4+/D8eo+EOws7MzkJGagtk5RmSlpmDYJZ4f/erVHgZ7U0THoHo3JFbsUaxs27YNZWVluvdvbm4OubU+wGCPiIgSTdJoLdltAUbtQIYpOrenFYwNd8trutYAWP5DrsaasQa4/bGJtocAcPV3wcdKbuCtbwN3flOunGp9GUhJF78R7hqW22+Kgp321+T5YKJ2bnqDPUAOr9b+hRw2qgV7HqccUi4o13/uZCWq2AucH7jiweBgDwivXSQgt3aduTr049oF1XVacuYpX5eaBSz7FPB+bfB1559XDotFes8qz6H0mr9RrjYMDPa8x4mqBwGg6Fbg3n8G2hqBtlcn9s8oBMwfl+fnxUJ6nngOoYjeij1AnocoklciB8rz7gUu/ky8T+YkqtjLLQbK/0VuP+v9PZSSIf9+s12M7m3Nu1uufNMd7BmADzwGFC5Xf47oZVoa+TmIdCovL4fZbPar2vP9Wo9Vq1Zhw4YN0V4aERHRpCBJEmrevqwY6gHAgvwsAIDRYMCG+YV4pVVc1WcbdaJvZAyFmekxWSvFhyRJ6BxS/0DikNMNp9uDtBROKqPoKi4ujssH6fjIJSKixBrsELdC85I8clARyRwzL635dIAczp3+IXBy/0Tg0HNanl3nNdYPdCm8me9xAW/8E9D6WwCSenVL26tycCliE8x9AkIL9kZtQM/NNo9aswO1qrMmC6WKPV+mxcDcu/WdL7MImC2YXedL1IZRj57ToR+jFVoseUAO+AK1vyZXFuql9XgwGIHFnxRXnI30yiGpKOzJNwMb/1Feo/njwMZ/AD70/+R/G/8hdqEeIIdsSnP2Aumdsacmb6H8X7U5e2kR3ka8FSwF7nh84j4r3w9s+Gb0b2f+vcA8jfmEvtY/CpR8WP460mAvc8bkClxpSlizZk2il0BERDRp9Y04cdk2qLrPgryJ10h/tm4R5udmKu5rtQ9FbW2UGP1jLoy4BF1VAjjGNMacECUxBntERBRf198A3ngSePPbcgtKPUHVlSPAmR9EHu7pmU8HiKtrOt8Ghnvkr6+/MdGmMxIdbypf160Q+gyEEOwBEwGN1uxApTlvk42eij1ArtrTI2s2cFcVsHQrFP9sUgr2Rm3Aqe/JsxjPPe8fYI86AEdoFRkAtEOL9DxgyRbxdV2n9N2GJKkHe+n5wG1/KQekSsFe7/vi9pQlHwPScvStIxb0ztkLpWJPxJgG5MyRvy5aId4ne/ZEi9jJLHu2XAkZLalZ8qy87FlyBZ6INxBNNwF37gEWb564LtJgz7QksuOJwhDYdpPz9YiIiPTTarkITFTsAcD8vCz8e8Xt2HWbeDZyi0ZISMmvQ2O+npd9hO04afJisEdERPFz7RXg9X8A2o/Lbfhe+Zr+SrELP7tZBReBUKrdhMe3yv+NVnWbKPjw6jkj2F8Sfw/zNgKf/LH4PO2vysdpVeyN9EQnrEwkSdJXsQfIYcus9drnzCwCUjOBtV8GPvVLIHdh8D6iYM/jAhr2AJdfkFuuNv8X8M6/TVwfyuwwX3pCC6WZlCPidjNBut4DhgQz4RbcB3zqV8D9tYD5Y/K2DEFlk8cJXFcIrWcmuCpFz5y9tFw5IE3LBhBm8Ja3cGI2p8EI3PLZ4H30PP4mA0MKkF8SvfPN3TDRhnjhh4KvL1gGbPkf4IGfyb/3Ftznf31uhC1PCtiGk+KvsND/d2lJSRSfU0RERFNcp44Qx7diDwDSU4youEX82qqFFXuTXqeOsBcA7JyzR5MYgz0iIoqfy/X+lyU30PqS/uOvHIns9iMO9q7KLQY7347sPHr0nAkO2kZ6xHPg8hYCGQXAwg8HXzfcJVdGalXsSR5gxBbuapODe0QO1AKJKvYAYMU27XP6tuQzpgB5gtBg8GYINmoHrv4euPoHOcQOrK68+oo8wxFQrsjUoifYyywCDIIxyiO92sf2nQfeqBZfN/9ewJgq//O9LRHR8zolM/Gzy/S04vT+jA3G8FtlBn6fKx6aaM0JANlzgVv+OLxzJ6NoVrnN92nHurgCmOEzvzItB1j3Vfnr1CxxxWPmDHE7Wr0KloV/LFGY+vr8Z5qWl0+BmbdERERxojVLDfCv2PPKSk3B3JzglpxXbAz2YsHtkfCT96/hb/5wBv/+7hU4Yhiq6a7YY7BHk5jgXR8iIqIYEVWhKTGkBAdbDqscQBnC/FxKNIK9G2+pzwSMFtcwYL/i/yazo1W8r+8sr6u/C77+yhFx4BVouFv/DLJkJKrWA5RbKs4sBYpuVW9DGjhrSxSsjTmA7jPAW9/WCM88QE8zsKA8vPl6SrcfyGAEMgsmQkSvkT7h7uMGO4BX/wZwCV7IpmQAc+4QrGeO+FyiAHrGrXI4mkh6WnH6/ozTcwGnoL2rlsCqr7Rs4MM1QMdbAAxyWBXpDL9kUrAUCKOzbJCUDGCOz0xLYypQthfofAdwDgKzbgMyBBW4vgwG+Xdin8KcUi2JDp9pWrLb7eNfV1RUJHAlREREk4+eVpyzs8Uz9RYVZONGwPFW+yAkSYJhKrTNTyJPv3kBv7ncAQB4vb0Xb7T34rn7b0OqMfp1R3qqOAEGezS5sWKPiIjiI5QwbN49wLLPBG93jwJDGpVnakKdTxeo/6o4OIuVwKou2wXxft5gb/YH5DfGA+ltYTrcBTiH4hNcxoJovh4gbsUJyAGAVtVeYEWaUrD27v/VVxHnaJFDL9vF4OtE952v9Hz9YZDS7Ds15/9HOcS65bNyS9JAM0vleXJ6+FZeJYqeVpy+93G4zwVRO0djCjDvbmDeXVMr1AOiF4ateCi42s5glEPlBfdph3peeWG2MUzLlWcGEoXAarVi48aNfuFcqI4fPz7+9aOPPhqNZREREU0bWtVZC/OzkGIUh3SLTMHzvwedbnQPj0VlbSTrH3Pi5Sv+40Gs9iG8eyP8v5/UdA7pbMXJGXs0iTHYIyKi+BhVqKYSyVuoPOeoX6FqTYvSfLpQ9J2T56XFi29Vl3tMntcWxADkLZC/TM0UV1Xp9eaTQP0fA7/+U+D6G+GfJ1GUKvaUWnEC8s9LrfWe3mBv4Jr62rxsl4Des+L5ird8Vr0aNZTZYRlhBHt958Tb598rBy4iqVnA7NvF1wWaWapvv1gKtWJPq8pRyBDd1pSTgWkJVOcRFq5Qvm5xhfwYu+NxYLlgFmE48gSzMPUoWCpu70mkoqCgAFarFdXVCm2MNVitVlitcsnr9u3bUVqaBL8riYiIJhGt6qzlRcofqltckC3c3mIbjGhN5K+5qx9uSQra/pP3db6ODhFbcdJ0wGCPiIjiY9Smf9+8hcpvzIYbzinNpwuFW+GPw+Wf0662Ckf3aTmQBOSZZaJgZmapf4WL2hvoeo30Aif3y63vJpNQK/YA+U385Z9Xvj4jsBXn/NDX5ct+GehqEl83506guEz5WLXrAgW2EAXk56AoUATkx9ng9eDthSuBO3ert9Ccv1F7PYZUoHC59n6xpifYy/UJ9gLbAeuROz+yGW+TUWomkLtA+fqiW5WvW/9V4K4qYOGH5BbM0RBusMc2nBQGk8mEiooK1NXVjQd0odizZw8AwGw2o6qqKtrLIyIimvLUgj2jAdi6XPk1nNmkEOzZOWcvmkShHgD0jcSmMlLP3EUAMZ3zRxRrDPaIiCg+Qg32coshrADRqtgb6QXe+Tfg5UrgpV3Ab78MvLUXuP66eP+sWfrXJZKSASz7I+1AzZAit5ILpR3hmAMY7ZPn453/qXif5X/sf1nPDDY9XEPKP7NkFU7FHiAHU0pBQGDFXvas8Gc8AnLAfP754O0pmXK10NovA3Pv9g84UrOApZ8Gljyg/3ZEwZ7klsPPwetycNv4DeDsIbkadMwuDr5nrdVutTnvLu2fSeHy2ITfocoohOafv77PIaXQUi0gVKsAncoKVKoUixR+P8aqijOSij2iMOzYsQPAREinV01NDRobG2EymXD06FGYTKZYLI+IiGjKGnS6MOAUz5P/oHkWvvPRdVg1U/mDngvzsyHq0slgL7qUgj2l7ZEYdblh09li08Zgjyax1EQvgIiIpgm9wV5qFpC/CEhJB3LmAIM3/K9Xq9gbvAE07gaGuwOOaQWuvSI+Zs4dQMtRfWsTWfxJIMMEzFwDdCtUYn36BQBGuTrs/E+BnjP6zz9qBzrfA4Y6gq8rWBbcBjE3SsEeALT8Gij5SPTOF2tKFXtac7kMRnnW3sn9/tvzFgYHe8ZUeQZX4OMyUrPWyufOMAH3/K0ctnmDtrRc9Yo5EdGMPQAYaAferJ6o/uxuAjrfAVb+iXh/PUFxeh4wcy3Q9Z7yPnqq+uLBmCKHniM94uuzZvnP4Sv5GND+mv8+M9fKP99rfxCfY7q14fQyLVX+PZs9B5i1PvgxEkpYHYrsufLzySN+k0fIkArMWheb9dCUV15eDpPJhMbGRlRXV+uqvKuurkZNTQ3MZjMOHTrEUI+IiCgMStV6X719CT69QnuUQXqKEcV5Wbjq8P+QI1txRtfgmPjvcpcn+sGe3mo9AHCMhvB6gSjJMNgjIqL40BvsLdosh3oAkLtQHOxJEtBvBW68BTh9Pkl37ZXgUE/L7NvCD/aMqXK1HgDMWCPep+hW/+qrmQr7KRlzBAcLXssfDJ4HlT03tPOrCacNoS/bJaCnWa5u0lPVpWXwujz7L7MQmL8pOOxSqthL06jYA4AFHwS6TgHW38iXU7OA0l3ieVs586If7C39tP/llPSJ50E4AluIel34aXBL154zwKsKb0LrrQCdf69ysJe3EFiyRd954iFrhkKwZwTWfcX/Pp9zO1C8CWg7Ll/OLAJW/6k8/1Ip2JuuVV9qlYoZBcCqLwJvtE48/ubdA8y/JzZrMabIVd+OENoirv6SciBOpMOWLVtQV1eHmpoa1NXVYfv27SgrK4PZbAYAOBwOWK1WNDQ0oL6+Hna7Hdu3b8e+ffsSvHIiIqLJSynYm52Tqfsci0zZQcGe1T4EjyTByPnLUTHkFL+3EJNgT+d8PYAVezS5MdgjIqL40BPs+QZlAJBXAnS85b+PcwC48iLQ9FzkwVPWLPk2wmX+uBwSAEDRSrmdonvEf5/AireCZXJLQqV5fYHG+oEhQYiUM1/8pnhatvwmeiitTxVvWyEo06PlGPDuMwBuznSbexdw99+KgzI9rr8BvPFPEzPiil4Ayp6SHzPj6xVU7KXl6Kt2MxiA2/4SWLoVGOyQWwcqvcmfMw/AuyF/C4oKV0S/Ukhp7ddPhHYe3cHePcCp72H8/vY9/t4nIwspo820FOg7H7z9A38FzLvbf5sxFbjzm8DSs4CzXw7w07IBt8q8zuk6p00t0MwoAHLmAh/5HtB3DkjNvvmhhxi+UZJXohzs3fe0fJ0kASlp8nMwT2VGIJEOX/jCF1BXVwcAsNvtqKmpQU1NjXDfsrIyVFVVobQ0Ru1oiYiIponOoRHh9tk5+scALDLloPGq/wf/Rt0e3BgYwfy8aTY7O0YGFdqluj0KM+Aj0KEQ7C0rzMHFPv9KTMeoE5IkwcAAlyYhBntERBRdwz0TFRnpeXILNoMBGLFpH2v+xERQBijPSTr1bMTLlM9fIrdVVFK0CuhtFl9nMAK3+My3S82U5929XzuxLXsuUPJR/+OMqfIb2mptC32N9cvtOAPllyhXwOXMi06wN3gd8DjlGWvOQTmMzDDJFYiSW15XSqYcdPhyjwFnfgi/kOfGG3KoFE5LRskt3+eSz/l6m+VKxgXlN29zVPwmvtZ8PV8Gg9xGUauVYrTmGHqt2Bb9gEM0Yy9UxjT/56Pq7RUByz4DXPzZxLbs2cCmJ/WfI16WbAGu/s4nXDcA6ysB80fF+xsMwIxb/bephXcZ07SdntpzLTVzYp85d8RnPWpz9opWyv+Ioqi0tBTNzc2oq6tDQ0MDWltbYbPZYLfbYTabUVJSgvLyclRUVIxX8REREVFklEKcOaEEewXZwu0t9iEGe1EyoNCK0xmTVpzisPeWorygYM/lkTDkdCMnnREJTT581BIRUXQ4h4C39gZX2JmWAHf/DTBmUz8+MCgD5PAqlvIWym84Zwpa8y24T27HqBTsLfiQXIHia8VDcvu3jnfkYOWWz4orlWasDiHYcwBjgmAvXSU8yJkH9L6vft68Enn2oBrJI89k63gbeP9HchBSsAxYuR04d0iuejKmyy3sln164jj7ZXG137WG8IK97jPiFqvdFjnYaz8BvPt/xbeZrjFfLxzRDPZMS4C5G6J3Pq9oBHvZc0Nrn7rmS4BpEdD5rhzo3/JHcsVksjEtBj70XeDyC3Jwvbgi9PaZ6XnyzyewmnZejFpLThbzNgLXA1oH5yUowFAL9ohixGQyobKyEpWVlYleChER0bQgaruYnZqC3DT9b3mbTeLXLC32QWxckGQfUpykBhVacQ46XXB7JKQYo/dBV9FjIs1ogNkkDnDto04GezQp8VFLRETRcf754FAPkEOeU89pV+wtrgBy5vhvi/Ubs942nCUfBs7/xP+6pZ9SCccMwIrPCzYb5EBwwX3qtxvKnL2hTsAj+HRbhkpgpSd4mn8PcE4j2APkOWLnnp+4bLsIvP4PE5c9Y4DloNxK0rT45j6XxOfqeEuu5gu1LWNgUODV+rI87+ztfwFcCq0RQ6nY08v7fUbDrTti047QmCaHmpG0Uw0MrrUYUuTWs4HtZ5NR3gJ5nl4kFn8SOPODgG0VkZ1zsluyJfj5uvCDCVlKRG2WiYiIiGhSEFXszc7JCKm1YnFeJtKMhqDqsRbbUMTrI5lSK06PJF+Xn5EWtdsSPyYyYVK4Dfuok5WZNCmF8DFsIiIiHyO9wNvfAX73CHDyX4KDMV833gKGOsTXpWYBi+4HSv88+Lq0HOVZYUqM6UDxJmDVF7X39QaHK/8EWPKAXAWXNQu47a/kNm1KweL8eyMLHQtX6N934Jp4u1bFnprULGCOziox31BPTVvDxNd2hWDPNSxXc4VC8sgtN0Uy8uWwUCnUA2JTsZc9V18lWlquelvPZZ8NnukWTRkRVu3lzo/OOqaqpVuB5Z8D0vLk31PrvgLMvi3Rq0qsWeuA9V+Vq6AzCoFbPidXbiZCbrF4+3SvqiQiIiKaQjoHg9suhjJfDwBSjUYszA+u5rLaGexFi1IrTgCwjTijeluiir05ORmqwR7RZMSKPSIiCt1QB9CwGxjuki/bL2sc4AGcA8GbF20G1j+qXrGUt3BiZp+SBfcBH/gr+Wtjqlw51H1GY02YCOdSMuQ35QMreGaslgMcp28fdgOw4kHtc6tJzZRbUioFVr76FYI9tTleWsFe0a3B1ZGROvf8RJiqVLEHyN/zvLv0n7fvgrgNJyDPc+y/qn58LCr2DAZ5xlp3k/I+K7cDt24H3E7gpYeDv4fFFcCaP4v+2nxlFgL9grmDekV7luBUk5IuV4yu/tNEryR5GAzyYzsZKhdT0oE5dwZXkgfOPSUiIiKiScnl8aBneCxo++zs0II9AFhkysZlm//8tauOIbg8HqQaWRcTKaVWnADgiGKw5vZI6BoSV3GaMsUxiH1UOXQkSmb8zURERKEZ7gGOPzER6kUiw6TdhlBPZVxxmRzOpWTIoZ6e49JN6u0sAbmybe2X5baGAAAjUPoXoc/jEln9Z0COT0XU3LvEreNG+8THq1WiaVVazVh982cfgz8DPC7AcUX5+uuvi1uLBnKPAi2/Bl75a+V9JDfQdUr9PLGo2APUK/EAIHu2/N+UNGBdpU+Fn1Gu1Fv3ldi04PQVarVrIAZ7NNmt/pJ/S9kFH4zNTEsiIiIiiruuoTFIgu1zcjJDPpe5ILhiz+mR0NYfXBFIoRtUq9iLYrDXOzIGtxT8qJidrVKxF+WKQaJ4YcUeERHp53EBJ/4OGLwenfPpaRVoWqZ+fUoGMPsDgnPny+HdmF18nN5WmiUfkdvL2a/Is9WyZuo7TkvufOCj3wN6muUAseAW4NUqoF/H3DtAvWIvPV8+p1KLyplr5AA0c0Z0AlpAvh8AuYJOLbhz9gPdp4HZ65X38biAxj1A33nt2+3RqMw0RSGEFdEKd73BHiC32/zoQflnkzVDuUVgtGVG2IqTwR5NdqbFwEe+B/Sek8P1aHwog4iIiIiSgqgNJxB6K04AWGQSj1posQ/CbAoO/Sg0Q3Gq2BO14QTksFcp2LsxOIKD715Gc3c/lhTk4IulZpgyozfzjyhWGOwREZE/56BclZczT6428tX6so62myFQC6e8ijcB5w4pz+ibc7vc2lIkbyHQE2GwB8hhXrQCPV/GNDk09Aqlukxtxp7BIN9/ovvKmDox4y+zMHrBntspB3JqbTi92l9VD/auNegL9bTkLwbm3BH5eURCCfYA+WcdadAWKj0Ve+n5wJhDfF12lNu1EiVCSgYwa22iV0FEREREUdY1FNyGEwivFediQcUeAFhtQ4CgsQ7pJ0kSBpzKH/6NZivMDpWwNzM1BRkpRoy6PX7XvXBh4oPrZ7ocaOq048D9H0CKMcYddogixFacREQ04dILwNEdwMtfBn7zZ0DvWf/r2xqje3t6KvbSsoGN/6S87/x7lY9VC+9CCfbiJZR5cFptRJVCmcIV8uwpQF9LTN08ciBsv6i96/UTgORRvr7z7ciXs+pLwAefBowpkZ9LJHchYExXuNIQmyA4VFrPr6yZwMIPK18fGOwTEREREREliW7BLDUAmBVGxd6cnExkpgS/Td5iHxTsTaEYc3vg8oiapsqi2QqzU+ExMefmY0Kpas+X1T6EM90KH34lSiIM9oiISNZ7Fmh6Tp5tBgAjPcDb35kIYMb6teeZhSqjQN9+eQuATdXBFW05c4F596gcpxbsJeHH7vRW7BnTgRSNuQFKwZnv9+0c0nd7eg136qvYG+mVW+Mp6T4d2TpKdwIrPj/RHjQWjCmAaZH4uswin7mMCaRVsTd/k9yWlYiIiIiIaJLpHg4OcQwAijKVPoCpzGgwoETQcrPFHuXXzNPQoEobTgCwx7gVpwHAzCz9wR4AXHXwfqfkx2CPiIhkZ38MBI6eHrgKnPwX4OWvAkceBCT1P8jG6Q1U9LTi9MpfBNz3r3LrzYxCYNZ64K6/UW7DCaiHd5O5Yi8jX263qWbuXQrb75z42vxRfben12CH/lat7a+Jtw91RN4eNF6z4ZTm96UmyQwGtWDPkArc8kfAjNXi682fiM2aiIiIiIiIoqBb0IqzMDMNaYLKOz0WFQTP2WvrH8aYW6XbDGkaGFPvFBTNYE/UirMoK338MZGfoW8qWd9w9NZEFCsM9oiICLBdBDpOiq+79gfAcSW085Xu0t7HYAyt9SQA5BbLbTk/WQdsehIwLVbfXy28S4ZWiYH0VuypzdfzmnsHYAhoQ5lZJAeiXsXlupemS9cpwDWsb9/rrwKSoB2HUrXeHY8DC+7Td+54BXtKc/acA/G5fS1qM/3MH5WfAxkmYPZtwdfP3xi7dREREREREUWoS9B2cUYY8/W8Fgkq9jwSq7ciNagyXw+IfcXeHJ/WrIsF4a1I74h4fqNeLo8H7f3Dmt87USQY7BERTTV954H364DL9YBb5x9I554P77byzMCHn/Vvqbn4k8CiT2jP90o3yeFeLKmFd1oVb4mgu2JPR7CXNRMo/Qu5MgsA0vKA9Y/6VzjmLQBWfXHifsgsAhZXhLZmX0ozGNME39fgDXFgrBTszVijM7AzyC1a4yF/kXh7srS3TM1Wrp695XMTX5fuAnLmT1xetFmujCUiIiIiIkpSohl7s6Ic7AHANYfOD6+SkFYrTkeUgj1JkoTB3myfx8Snls/XVbVniyDYO9vdjz8/8g6++MJJfP5/38CLF6+HfS4iNfrqT4mIaHKwvgS8828Yb6nZ8mvgvqeBFJU+4o5WoP3V8G5v/ka5au4T/wX0nZMDvrwF8nUFy4COt5SP1TtfLxIGgxxQdLztv33pp2N/2+HQXbGnc7+lW+X7aLhbrnYUBYcrHgQWfhAYtQO5C4DUDLnybuCa7mWP8yj88bviQeD0fwRvb3sVMC3x39ZzJni/7DlA9ix9wV7WDCAl9JkKYSlYJoeWzn7/7XorC2PNYJDXGPgzXfghINfnZ5lfAnz0e/J8xLTciecwERERERFREnJ5POgbCQ6EZmaF/1pwbq54zIfodhKhc3AUV2yDWFaUixkRfJ/xNqjRitMWpWBv0OnGkCs4RJydM3G/zs3NxNMfXYu/eaUZ1weC23Z69YZ5n7s9Ep5+8zza+uUweMztwf996yLumFeE2Tnhh85TiUeScLanH063B2tmmZBiTMIP3U8SrNgjIpoqJE/wnDz7JeDGG+rHtf42/Nv0tutLSZOrlHwDgYIl4mO8MgvCv91QLH8QMPoEm2m5wLJPx+e2Q6U3sAtlNmHWTKBopXo1YPYcoHA5kJYtt+9c/nn959cy+wPA4vsBo+CFx/XX/S+P9IkDRe8cOD3Bnm/lWaylpAMrH/LfVrBMeb5hIiz/3ETVJgBkzgBW/2nwfsY0+XHCUI+IiIiIiJJc7/AYBIMdIqrYM2WIPxAdrYqySBy7dAP/36/ewrdeOYPtv3gTL1yYPFVgWu0oR1wejAoCuVCJ5usBCArUzKYcfPfj63HbnALFc/UNh1exd66nH1ds/q1bPRLw2rXusM431QyMubDndxb85W9O4esvW/Dlo+8IK29JHwZ7RERTxVAnMNQRvP1yvfpxoc7P88qeG1xt5atgufrx8ajYA+TA8cPPygHfyu3Axw4C2bPjc9uh0tuKU28AGK6FH5TDvmhYsQ1IzZIDvkCOK8CYT7WbqFoPmGhtmasjtItXG06vZZ8GNj0lt7Ys3QmU/ytgTNE8LG7mbgDK98uP/1X/H/DB7yTnfEkiIiIiIkpqF3oHcOTidZzqsCV6KegaEgcvM7LDr2TLTU+FqHgoWhVl4RpyuvDMyUtw35xR75Yk1Lx9abwqLNlpteIEAPto5LPoOgRtOAH/GXtepsw07P3wGnznY2uF93m4VZpv3+gTbj9+tSes8001v77cgfc67OOXW+xDqDvdmsAVTW4M9oiIpoqhG+Ltfec0jusM7/aWf159Tt3cO9Wrp+IV7AFyFdLqLwK3bo/v7YYqLUff3MFQKvbCYUwFVn8p8vPMWD0Rys0RBHsA0NPs87XKfD1AntuoNDPOS9ccviibtRZY86dyyKfW9jZRilbIj/8V2xjqERERERFRyA6fuYrKY+/i3968iK+/bMGBdy4ndD1KVT6RVOwZDQbkpwe/nkt0xd6Zrn6Muj1+21weadIEIgMarTiB6PyMRfP1AGBOjrjFqsFgwJpZJvzxyuCuNcMuN4bDqCK81Dco3J6VmkQf/k2ghtbgysX6izcgSaL6W9LCYI+IaDLrPQv84THg138GvP5P4n3ScpWPl6TQg73cYmDNnwOLPqG+nzEVuP1r8n9FMgpDu93pwGCQZ7ZpiXXFHiDPiVv/KJBXAuSZJ9phhmLFtomvlY7vPi3+2iujQH7MAfLPRyu4S0SwR0REREQ0hXUMjuDHZ1rx/feu4GLvQKKXQ3HWMzyG/7ZY/bb977k2dCq0PoyljsER/Mhixf7Xzwuvn5kV2RwzU2ZwsGdPcLBnGxFXJ750pRPOgMAvGWm14gSi8zPuHFJoxakR9hYI7nNA+eeuRJIknO6yC6/T8zOYDpq7HcLtShW4pE7h3VYiIkp6I71Aw25A0vgDQa2941g/4A6hn/XSTwFrd+nff8atwMZ/Bl77W8AT8D/qeXfrP890kp4HjIn/GBwX64o9r8X3y/8AwDUMHPsi4NT5Qr5gmX/7zXyzHDIHHu+t0nMOAnZBW9gZa/wrQ2etBRwtyrerp10nERERERHp0t4/jL986RRsN1vT/exsG/7pvtW4fR4/qDldvHylE06Pf0WNRwLe7bDjE0vE1VCxcMU2iMdeasKASkgyM4JWnACQnxH8Vnmigz2HSsXba209uK9kVhxXE7rBMT2tOCP/GZ/vCX6vIjctFTnp6vFHUab4MdM37MS83Czdt9/qGFZsKcrgSt2F3v6gWYikjRV7REST1bUG7VAPAKDSLnM4xGo909LQ9gfkIGbjP8gz+QDAkAKseVhuj0nB9MzZS49TsOcrNQu47f+oV4D6WrHNP5AzGMVVe7aLcmjY0wyIxp/PDDhmxZ+ot3hlxR4RERERUdS8eOnGeKgHAE6PhP2vn8cQK1Cmjbeu9wq3X+6Lb/Xmz8+1q4Z6eempyIyw5aEpQ1Sxp/1Yf/t6H5589Sz2nTinWLUVLrXQ68WLCiNZkkg8Kvacbg/e7+kP2r58hvb7F4VZ4oq93hAr9tTu957hUXimebtJl0e5uvQCK8HDwoo9IqLJquWYvv3UKqxCbcNZEEawBwCz1gEffQ4YaJNbK2by052K9LTZjFfFXqDiTfJ9OdInzwM89gXxfnlmcUXmzDXAjTf8t0luuaWs1nw9r4x8YNO3gcbdwFCH/3X5ZnldREREREQUFaLWmz3DY6g7fRV/cdviBKyI4q29X9zi0GBQ+RBxDLx9vU/1+kir9QBxsOcYdUKSJMXv9422Xvxtwxl4ixr/YO3C/o+sxepZ0RmhYR9RDr3euWFDe/8w5ufpryyLN7Uw1kvte9TjfO8AxgRtSdfouA8KlSr2Qgz2LJ3iNpOA/IEI+6hT8bamA5vKfXyewV5YWLFHRDRZuXX+kRGtYM+YBuQt1L9/oJR0wLSYoZ4WzYo9A5Cus2ouFtLzgPwSIGuGHNKKrPi8XKEXKDCk8+o5I56vl5YDmBYFb8+eJYd7OXMnthlSgJXbtVZPRERERDRtjbk9OPDOZfzFkbfxN6+cwSUdFVetjiHh9p+dbYPVLr6Opo6+4TF0DonHd/QMx7e9oNI6vCKdrweIgz2XR8KQU7mdZP2F6/DtVOr0SKi/cD3itXhpVbMdv9oTtduKBT2tOPsiDPYsCtVypbO1PxSt1oozpDV0qldq9kzzdpxq9/GFvgFI07yiMRys2FNht9vxzDPPoK6uDidOnIDJFFqFhN1uxwsvvICCggLk5+ejsND/zez8/IlPDTgcE6l+X1/f+OXS0lKYzeYIvgsimrKyZgJDOtouuIYBj1MO5gKFEuxlFABG/m8j5rQq9tLz5BArGeQtBEZt/tty5gHF5eL9C5YCKRnBcx073gZsl4L3L1ql/L3mzAU+8j2g5TeAawiYc0f4FaVERERERNPAv715AS9dkV8DttiHYOm04z8fuCOsKhK3JOHZk5ew98Nr4l65RfFzuku5Cqk3jsHegMqcOa9Z2bEJ9gA5XFOa1fZ6e3Cr0t+2dGLPxhURrweQKwbV6AnoE0lP294W+2BEt3FaUC2XajRg5QztUSd5GakwGoCAMZIhteLsHBzRDJ67hkaxrCiBH9JOMLXfF7YRJ7qHx6LyHJ5O+A6tgNVqxbPPPou6urrxbTabLeRg79SpU9izZ09Ea6mqqkJlZaXu/RsaGlBfX4+mpia0trbCbrfDZDKhpKQEW7duxfbt20P+PogoSUnan3oaNzYgrpQLZcZeyvRtGRBXWhV7elp1xov540C3xX/bmocBo0IYZ0wFim4Fut7z3953Trz/TIUKP6+UDGDpA7qWSkREREQ0nY243PiDtctv26DTjVev9mDLLeI51ZIkqc4Xe7fDhoar3bivZFZU10rJo0mlCimeFXudg+qhCRC7VpwAYBt1JqzdpU0j2GtJ8srZAR0Ve5f6BuH2SEgxhv4hAbdHEs63W16Uq2vmotFgQGFmetDjWa11ZKAzKgG4V/ew9mN4KtNqbXqhd4DBXojYitOHxWLBzp07sXHjRr9QbzKwWCzYuHEjHnroofG1r127FqWlpbDb7bBYLKiursaqVatQW1ub4NUSUVSMhfCpLKV2nKFU7C39lP59KXxawV2i5uuJlHwEWP9/gMKVQOFy4PavA/M3qh+jFdb5mrE6svUREREREREAeU6aM7AkBUDt6VbFY0bcHuHcKl8H3rmMYZU2hTS5iQITr97hMQw5XZqPkWjoGBTP+fM1MwqhQL5CsKdUNafWPjBaPxetir2rjiG4Bc/tZCBJEoZc2hV7o26PYttfLS32QQwKfgetmaX/vZPCzOD7PZSKVD0z4rrYilP1+vO9/XFaydQx7Sv27HY76urqUFtbC6vVmujlhKW2tna8MnD79u2oqqryq8qz2+14/PHHceTIEQDAnj17YLVaUVVVlZD1ElGUqM3OCzSm8D9IUbA39y6gvxUY9OkJn5IBzL83tPVReDQr9pIo2AOAxZvlf3otuA84+2NA0niRY0wHCm+JbG1ERERERAQAcIyJ31QdVGlxqBUoAPKb1T8+04qH1y8Oe22UnAadLly2KbdIHHa58amfnAAAbFk2F1/+wBJk6KiQCkeHnoq9KMzYKxAEPIBy9daQSznU7hkexbzcyKr83B4JDpWqWUCe6dfWP4wSU3ZEtxULwy53UItLJRd6B7C4ICfk27AI2nACQOls/d2O5HbE/o/1UOb+XdAR7HVrtOqc6vRU7FFopm2wZ7fbsWvXLjQ2Nvpt3759O7761a/i/vvvh92uPvRSi+9MvcrKyvHz2Wy28e2+s/UCrwOAkpIS1dtoaGgYD/UqKyuFYZ3JZMLBgwexc+fO8XCvpqYG69atw5YtW3R/P0SURCRJOawTEe3rGgHGBH8A5cwFbvkj4OS/AMNdQFoOsPbL4laeFH2aFXtJ1IozHLnFwIptcrinpmileC4kERERERGFrEehWsSo0vpObyu6n51tw7ZVCxVnkNHk1Nzl0B3K1F+8AbMpG59eURyTtXTpCEVmRaUVp/gxrBSuqc3+6xoaizjYGxhzQc9d0GIfTMpgT08bTq8LvQP4+JI5Id+GqKrUAGD1rBCCvazgx07fyBgkSdKcIeqRJFzQMedQz2N4KuvTqIC80Dug6+dNE6bt/3FNJtN4qGcymfDII4/4zZ8rKCiIONjzva1YVcd9+ctf1n0b+/fvHw/2AGD37t0M9ogmK/coIGm3Mxgnqu4b7greBgDZs4GZpcDHDgKjNiAtVw73KD5MSwBDqvL9W7g8vuuJhZXb5cfkpV8p7zPn9vith4iIiIhoiutVqJZIUXkTVU/FHiBXDJ3qtGPjghlhrY2Sk1q1nsgrrd0xC/YS3YrTrvBc6FcJ9qJRoaU1X8+rxTaEcvXakIQYdOp/3yrcVoyiGYOLCrKRl67/g8JFgkrNUbcHwy43stPU45P2/hEM6WhHHM+ZlMmoV+ODIn0jTvSNOFEkCFlJbNoGewBQUVGB8vJy7NixI+i6/PzoVUQUFBRE7Vy+ampqxsPHRx55RHN/k8mEyspK1NTUAJCrFmtra4XfPxEluVCq9QCg8115NtvMtUDKzf9JKs3Xy5ot/zclA8gO/dNSFKH0XGD554Bzh4KvMy0Fisviv6ZoMxiA0l1yO87L9cHX5xYDiz8Z/3URERFNQg8//DDS04PfBNm5cyd27dqVgBURUTJSqthTC/b0hgqA/uo+mjy0WkAGOt0lbokYDZ0arThXzshDbhQqRjNTU5CZYsRIwHw8pWBPvWIv8mBPb7guCreSQSjB3qW+Qbg9ElJUqohFhgS3MT/ESkm5FWew3mGnZrB3QWcg2TU0Oq0r0rRacQKApdMOGICCjDSsnW2acj+rAwcO4ODBg0Hbx8bCC32ndbAn+kFOJs8888z41xUVFbqO2bp163iwBwD19fUM9ogmI2eIwd7V38n/MgqBTdVA/iLlYC97dsTLowit+gIwcw3QbZGrM2GQ77P5G4G05GuvERaDAVhXCcy7++b3efMPmdwFwIJyVokSERHp1NvbK9w+MMBZJUQ0oUfhTdUhl1vxzeZQgp1Q3sCnyUFpLmMidKiEZKaMVOy8LXozHvMz0jAScHuKFXsqz5FuhTA9FEq3G6jFHlp1ZbwMKrTiXDUzD83d/u9rjbo9aHUMhTxnb8TlCdqWGeKsx0LF2YpjWJCvHhLqacMJyOscdLqjEkBPRn3D2o/lf3717PjX9y6Ygb/ZdGvIQW8yGxgYwI0bN6J2vun5SIqzaFb/edXX149X65lMJpjNZl3HlZaW+l0OnDFIRJPEWJhv1Iz2Ae9+F7jvX4HBdvE+DPaSw+zb5H9T3ewPyP+IiIgoLEVFRcKKvdzc3ASshoiSVc+wOBgZU2k3pzdUAIBBlcolmpzUQislwy43skIMVbSMuT3oFbQxXFGUiy+uXYRlRTmKFVfhMGWmoTMw2FOoSFVtxanwnAuF3udgW/8wRl1uvNthwxXbEG4pysUd8wojvv1IKQX+t80pCAr2AHnOWujBXnB4mJlqDOkcohl7APDcO5fxrU23Ym5uZtB1HYMjOHGtBz95v0337XQNjU7LYG/M7cFAiB/+ePVaD95o751SLZ5zc3Mxd+7coO1jY2OKH9RTM/0eSQkQi1acDQ0N41+vXbs2pGNLS0thsVj8zlVeXh61tRFRHITaitNX7/vASzuBgWvB16VkAOnR/zACERERUTT19/fDZrOhoKAAeXl5iV5OQn3/+9/Hhg0bEr0MIkpyvSrVErYRcbs5UZiRajTA7ZEgBWwf1DFjiiYXtdBKiX3Eiazc6AZ7SrPqykpm4s750Q+vTII5e+G04oxnxZ5HAh78+Rt+z8PtaxbiS2sXRbyGSPQpBKK3zS1A3ZmrQdsv9A7g40v0j4RxeyQ4PYG/jcKp2BMHe+d6B7Dr6Dv4zkfXYUnhROB4oXcAe35nCfk50j00GnJwORXYdLThFHlzigV7u3btErbJf/PNN/GZz3wm5PMx2Iuhvr6+mJ27vn5iJlFJSWjTUdeuXesX7LW2tkZtXUQUJ84IWyuJQj1Anq83xXpYExER0eR0/PhxNDY2wmq1orW1FVarFQ6H8vye/Px8mM1mlJSUwGw2o6ysDJs2bYrjiomIkpdSxR4gB3vz84LbzYlChYKMNAy73EFBHltxTj39YbTitI04hdVNkehQmK83Jye6t+MlCvaU2tKqBTtne/rxnTcuYMWMXGxeOhfGMN5rUaoUFAl8Tv5P8zV8dmUx8tLFbSbj4frAsHD7ihl5KMpMQ2/A93e+N7T3ukTVekAYFXsKrTgBYMjpxn9ZrPiH8lXj2376/jXV+/4Dcwvwzg1b0PauKIS9k5FSwKvlyMUb+KsNt0R5NVMHg704iHYrTrvdPt6GE5BbcYZi0aJFfpetVms0lkVE8RRJxZ4atuEkIiKiBLl69Spqa2vR2Njo90FEL0kK/kS2L4fDAYvFMn6sd7Z4aWkpysrKsGPHDixcuDD6CyciSnJDTpdwDpWXTaEqSBTs5WekwWgwCII9VuxNNeG04gylfategW0xvWZnZ0T9tgB5Zl+gAacLLo8HqUb/wGhAI/x88dINvHgJONszgMfuCj2gsIdxH3g5PRIu9Q1i/ZyCsM8RqesDI0HbirLSkZmagluK8vBGu3/7wVb7kOLMT5FhxWAvtIq9vPRU5KSlKP4ee6u916/NrKiNqK+NC2YIgz21D1hMZaJWunpEu63vVMNgLw58W3FardbxF6utra2w2+0wmUwoKSnB1q1bUVFRoTkvL7DCTu98Pa/ACj/Ri2YiSnJOhT8iPv6fwDtPA91hPq9nrNLeh4iIiCiKfvzjH6O2ttbvdYkoxPO+7snPzx9/jWWz2eBwOGCz2fw+/OirqakJFosFNTU1WLt2LXbs2IEtW7ZM+xaeRDR99Gi8qarUJk0U0nirmQLDFrWWhJQ4Lo8Hh89cwzsdfZibk4k/Wb0QC/KzdR0bTivOvjBb7qnpHAwOhwBgdk6Mgj2F6i3HqAtFAbPY9D7uj126gYdWL8S8EKsZRc/B+bmZsI06MaQjTA83UIkWUbDn/RmUmLLwRrv/dQNOFxyjLsX7IJDSBxYyU0ILhAwGAz5onoUjF28Ir3d6JLx7w4aNC2ZAkiTNx/md8wqRYjDAHfD3rFJIPdWFW7Hn8njg9khIMbKzmAiDvTixWq3Ys2cPGhsbg66z2+3jnyytrq7G9u3bsW/fPsVznTp1yu9yqDP8ol1BSEQJMCZoT2BIlSvuMsLsMZ+7AFi0ObJ1EREREen07W9/e7yqzjfIM5lM2LRpE8rLy7Fu3TqUlJSE9BrG4XCMt+48deoULBbL+OuwU6dOoampCbt370ZlZSUeeeQRBnxENOVpvbmv9KarQxTsZabB5Ql+M52tOJPTMycvjYcVFjjw9vU+PLv5NszUqHYbdbkx6lau8lSiVP0ZCVErzlSjIShkixZRK05ADtkCb1Nv+CkBePeGDfOWzQ1pLUrh+szsDDR1ij/Q5CvcQCUaPJKEG6Jg72YL1QWC9r8AcK1/WH+w545OK04AqLx9KQAohntvtMnz3oY0nhsFmWmYl5uJoqx0dAUEebYE3h+JFG7g7/RI6Bkei1mIP9kx2Ish7/yH+vp61NXV6T6urq4OTU1NeP7554VtNtXmSuhRWOj/pr/NZgvrPM3NzSHtX1xcjOLi4rBui4gCiCr20vPk+XjpIb45lZ4PlO4E5m4A0nOjsz4iIiKKq7a2NrS1teneP9S/5aPpxRdfxOOPPw6HwwFJkmAymbBlyxaUl5ejrKws4g8i5ufnY82aNVizZg0qKirGtzc2NuKFF17AkSNHYLfbUVNTg9raWnzrW9/CQw89FOm3RUSUtLQq9kThgdsjCeeKmTJSMSKoFBocYyvOZDMw5sKvL3f4besdceLAu1dQde9K1WPDqdYDYhNcdAqCvVnZGWHNrNMjXyXYCxRKpep33ryAQ2euIjc9BdtWL8R9JbM0jxGG6xlp+Oji2TqDvfAr9rqHRvHdk5dwtqcfSwty8Oidy0KqOOwdHoPTE9yBwXsOpcrRa/3DWD1L39+CowoVe+G0cExPMeKvNtyCv7xzGR76xZtBvzdfb++FR5LQN6z+GP+jFcUwGAzIz0gNCvaUZjVOdZE8Dq8PDDPYU8BgLw68LWFKS0uxdevW8U+cOhwONDQ0oL6+PqhtjMViwa5du3D48OGg8/X19UW0nsAXyuEGhVVVVSHt/9hjj+FrX/taWLdFRAFEFXtpuf7/1SOjAPjED4GU2HzSjYiIiOLj8OHDePrppxO9DFX9/f3YtWsXGhsbIUnS+Nw73/AtlsrKylBWVoZ9+/ahoaEBNTU1OH78OHbv3o36+no899xzrN4joilJuxWnOLAQTTY1ZaQJwwxW7CWf8739cAmClT9Yu1CxbK7q7LVkCvY6BK0458Twjf6CEIK9UH9ONwZHgEHgn4+fRcFH0rBOY/6dXfDzNGWm4T7zLLglCUcu3hgPj0RtL8NtxSlJEvb8zoJWx/D4eb7+2yb899Y7dbdFFK0H8An2lCr2HEO61zkSpRl7vgwGA+6aX4QXL/lX7vUOj+Fi34BimAgAlbcvwaeWzwcgrvzs15jJOFX1CsLQgow05Gekjj/GlFwfGMG6ObFa2eQWel0q6eZbCbd3714cO3YMlZWV459G3bJlC/bt24fm5mZUVlYGHd/Y2Ija2tqg7YEhIFtrEk1DTkGw5622C6Vi75Y/ZqhHREREMXf69GncfffdaGhowKZNm3Ds2DEcOnQobqFeoPLychw+fBhHjx7Fvffei1deeQX33HMPzpw5k5D1EBHFUjjBnlJLRVNGGnLSgusEBp0u4XxUSpwLvYL3DW565uQlYUtVr/CDvejOdHN7pKCqJ0Cu2IsVxYo9UQAeQaD9u5Yu1etHXG6MCFo+egOjDy+ajX/96Fr899Y78d9b7xRW04UbtF7qGwwKXDqHRnG6S7tK0Esr2CvMTEO2IIC71q8e9PhSDvYiizzuKi4Sbn/9Wq9i9dm/fKQUn1lRPF5Jmp8e/HtSVIE5HYh+LxRkpmFRQY7msaJ2riRjsBdjJpMJx44dw44dO1T3q6qqElbAPfnkk5q3EWlrTgaDRJPQmEIrTkB/O83MGcDi+6O3JiIiIiKB48ePY/NmeY7voUOHcOjQIaxZsybBq5KVlpbi8OHDeO655+DxeLB582YcPXo00csiIooqraod22jw9UpvQJsy0pAjeMPaIwEjKpUsFOzGwIgwLIoWtWDPah/Cz8+1K17fH2YAIapqi8T1gWFhO8f5ueJqr2hQqtgLnFcnSVJIrTgDne0RvK/jQ+05KFIomEvXG2bQevxqt3D7S1c6dZ9DKdibezPYMxgMWJAffD9e06jg8qX0OyeSij0A+MDcAqSnBMcmr7f1CqvPAKAg0/9D83nCij0XPNPwAxC9gt9zRVnpmJWlHdArPY5EnnvnMv6+oRmNV7sxFsaM0MmGrThjaMeOHZqBnq/KykrU1tbCarWOb7Pb7bBarTCbzVFbV0FBgeplvaqrq7Fq1Srd+3O+HlEUiSr2QmnFmbsAuOPrQGrs/hgmIiKi+Nm2bRvKysp079/c3Bxya/1wHD9+HNu2bUNFRQX279+ftB8q9HZV+frXv46dO3fi4MGDuP9+fgCKiKaGcCr2lAIaU2YactLEb5oPOF3IUriOJgyMufA3r5zB6S75g/ofXzIHf71hGVKN0a2/UAv2AOBHllZ8yDwLMwXVb8nSivOKXdyWcVGBeD5bNOSmp8JokMNqX7++fAOfv3XB+GN8yOUO2icUl22DqtfbFeax5WeI384vzAzuxhQYRurlVvi+3CF8w6JAJj3FiKKsiXUuyMvC+YDHaVv/MDySpGuG4og7NhV7makpWD/HhDfb/cdhXewbwNpBk/CYoiz/IE90P3kkubo5L10czk5VoirHwsw03DorDzinfqzeYM/l8eClKx1wjLrw6rUe5KalorxkJj6+ZI7umY2TDYO9JPPUU08FDW5vbGyMarDn2yI0EqtWrcKGDRuici4iCoHHDTgFfwCOV+yptOJc+BGg9M/lfQws2iYiIpoqiouLk+6DdKdPn8a2bdtQWVmJJ554ItHL0ZSfn4+DBw+iuroaO3fuxLFjx7B69epEL4uIKGJawZ591Bn0RrpisJeRhlxBxR4ADI65YtoicTJr6x9GW/8wlhXm4uk3LoyHegDwm8sdWD0zH59cNjdqtzc45kK7xhviwy43/v3dK/jmvSuDrlMK9mZmpaNb5fFkG3VCkiQYdIQyelhtCsGeKXbBXorRgBVFeXg/oKKua2gMDx85iUfvWIbVs/IxrNAGUq+ZWepjUZRC0lAq9mwjY7pDMj1COY0okJmbk+G3FlHFntMjoXNwdLyyT02sKvYAYP2cgqBgTwLwfndwpWWa0YDcgBbF+QrhnWN0egV7oy43hpzBz5XCzHSsm21CeopRtbruumDGpsjJ631w+IThA07X+JzEqRrs8V3dJFNeXh4U4vlW8AFye09fkQZ1yfqpWSJSIKrWAyYCPbWKvbwFQIaJoR4RERHF3OOPP46qqqpJEer5qqqqwre//W3s3Lkz0UshIooKrVacHgnoD6gOUgv2RDP2AGBQ8OYtAf/TfA1/Vn8SVX84gwd//gbeaO8N2ufk9T7BkeG70Kderef1O2sXTnXYgrY7FKrFHr1zmWLFJgC4PFJUHwct9uAPNacZDZgXw1acAPDpFfOF27uGxvC3Dc340gsnceJa8P0YCvvNEFSJYitOQYAHwK8SzssjhTfXbdglvv9DaSN5YyC4pWbg/bYgT3w/6p2zpzhjLyXyYG++QrDY3B08EqsgMz0ozFac1TjN5uwpVY0WZaWjIDMdXyw1Qy0vto04Mazjd8rLCm1iP7J4tp5lTkp8ZzcJac2bKCws9Lsc6oy9wP3Xr18f0vFElGBKwZ430FOr2MuJ3icAiYiIiNQ8//zz+MpXvpLoZYRlx44dnLVHRFPCkNOlq7LIFvBms9Lst/yMVMVgZ9AZ/ryxqer6wDB+cKpFs2VjuLPQlGi14fR16MzVoG39Y8H3f6rRgHuKi/DvFbej6t6VuGNeYdA+gFwlFi0tglacZlM2UozRqUBT8iHzLKydLW65CMgVjc++fSmi23B6JNXnplq4LhI4480rnHacStWCSoFvoBGXWzhXbV5AWLYgX1x5qXfOnmKwF2ErTiA4hFQjqpZUapka+CGKqU7pd1vBzZ/Z51ctQM3m27DnnhX46CJxCHdDo2pv0OnCa23BQfvs7AysmaLVegCDvaQUWLEXeDmwwq6lpSWk8/f1+X8KqKSkJKTjiSjBxhQGLOtpxZkzL/rrISIiIhKY7J1BJvv6iYgA7Wo9r8AwRjTfKyctBalGI3IUWnEOhDmXbSo7ca0Xbh1VTj1Do1G93cC5ZQCQYjAgW9CisKnTHjQ7TdSKMy89FQaDAbOyM/BB8yx8XKESJlpz9pxujzDgMZtyonJ+NQaDAY/csRQxzg8V5+gBQPew+DFRoBDsFSlU8un9HeBL6T7Ue9/eUGgDGxjsFeeJq+LadFfsBbdwNECe5Repubn62woXCUJVpQA2nArKyaxP4fHn+zNbVpSLjy6ejfvMM4X7as3ZO361R9jO88OLZkWtDW0yYrCXhAIr8gKDt3Xr1vldDrUVZ2DFXjTn9xFRHGhV7KWo/PHBYI+IiIiIiGja6BjUFxgFVuyJ3nz2vlHNVpz6NXXaE3K7ooo9sykbnxK0mHR6JFwPaJsoqirKCwh0FSvEohRcXOsfFoaiiwpiN1/P1+KCHHxmRejzi5/YuAJPf3QtXnzwXiwtVA8hA59n53r6cfDdyzh05irOCma55aWnKgbrhdGs2FO4D22j+kJCpSAmcG5edloqZghaiEbSijMj1RiVGY/ZaamKIWqggiz9FXuOJP4AhEeS8NsrnXj25CX85nJHUOAfDqXHn+jxGhj8eqkFe5Ik4cWL14XXfUShAnCqED/CKKECK+rKy8v9LgcGfadPnw7p/K2trX6XS0tLQzqeiBJMq2JPjZ59iIiIiIiIaEr4Q2uXrv0CK3FEb+xPBHtsxamHJEk406VvfM7AWPRC0cExl7DiaXlRLlYU5QqPuWIf8muLKGrFmRcQchQoVIgptXENVYsteL4eACyOQ8We15+vXwT7iBO/bRHP7xJZNSsfc3LkgOLrdy3Hk6+dxVWF1pK+z7tXr3bj7xvfVz230kw6ACgUBGQA0BdGa1Sldqq2EXkuoFZwphTEiIKbBXlZ6Amo6rrmCG7BKiIK9jIFVanhmpubqRhy+hJV7OWnT76KvaffuIBfX+4Yv3zyeh+euHdlROdUevwVCsJQ7/MmUOAHD3w1ddrRLAjBlxbmYFFB/H5XJAIr9pKQ3T7xaZ6Kioqg600mE0ymiT7PVqs1pPP7tu4sKysLfYFElBiSG2j5DXByv/j6NPEf6ERERERERDT9DDpd+ENLeMGeKJwx3QxylCqGBpO4EiURrjqGdYUCgHxfeXS07NTjYp+4y88tRbmKbSwDQzSlVpy+lIK9aLXiFM3XA+TKw3hJNRqxZ+MK/Oyzd+s+Jtfn57SsKBf//snb8Q/lq4T7ekMel8eD/3dSe2bfgnyVYE/h/gg12HN7JMVZek6PhCEdlbl6K/YA8ffUMTiKUR2zQUWtODNTohfsKVWQBRL97LPTUpAiCECTNdjrGBzxC/UA4PfWLt1tUZWIKvaMBnHwmZmagiJBQN3er1yx92PBjFAA+MSSOSGscnJixV6UWa1WPPTQQzh69Khf+BaK48ePj3/96KOPCvfZtGkTjhw5Mn7ZYrHorrxramoa/zqwGpCIktjFXwKn/0P5et9qvKWfBi79wv/6lX8Si1URERERRc3x48fR2toKq9UKi8UCQB494P3wo8lkQkFBAQB5Bt769etRUlKCkpISrFmzJlHLJiJKSr9r6cKIYO6QiG+FzpDThS7BzDdv67TMFCNSDIagNolsxenvdJf+NpwSgGGnWzE0DUW7QqiytDAX83IzkZ5iDJpHFRii6WnFmZ+eBgPktfvS265RiyjYy0pNwewc/bPPoiU/Iw2ls/Jh0ajANBoQNMcwxWjAYoXKIW/w+0Zbr65ZeMUqFXuZqSnITk3BUEAg1jscWpDkGHUG3ae+bKNOzcdpj2A+YEFGGrIE1XSiKkQJwBXbEFbOVO86NeIWVexFr45Jd7AnCKMMBgPyMlKDgu5kbcX5irVbuL2htRsPrV4Y9nlFj+uCjDSkKAywnJebGXSM0u+0cz39eOeGLWi7KSMVm5fODX2xkwyDvSgrKCiA1WpFdXU19u3bF/LxVqt1vAJv+/btimHd1q1b/YK9xsZG3cGe9wWy9zaIaBJwO4Fzh9T38a3YW/opoPMdoP9m613TEmDJA7FbHxEREVGYfvzjH6O+vh6NjY3C66WAN4592z/5viYC5I4k5eXl2L59O/Ly2IKciKYvSZJwRGHukIhvZdnFvkHhG/tLbs42MxgMyElPCarqCbVir61/GD9ssqKtfxgrZ+Thz9cvQrbC/L7JqKlTHAI9uGoBnm++FrR9wOmKSrDXqTBXcX5uJlKMBpjzs3EhoKrP6hOiuTyeoHAICK6wSTEakJ+RCnvA4yBqFXuCVpxmUzaMUZifFo5binI1g73c9FRhm0qlWW3e6q0jF2/oWsNClYo9QK6iHBrwv+9CrdjTqjK1jThVA0YA6BU8BkSVWIAcOItc6OvXDvZEFXtRbMWpv2JP/L3lpwuCvSSt2OscEodnog95hEJUsac0nxOQg97AFsbXB4bh8niQavQPbV9WaJH7mRXFwhB5qmErTgUOh74e2IFMJhMqKipQV1cXcotMANizZw8AwGw2o6qqSnG/LVu2+F2ura3Vdf76+vrxrysqKsKuKiSiOOt6D3CK+8sDAFKzAKPP/7Ry5gAf/Dfgnr+X/33w/wIZfL4TERFR8njxxRexevVq7NmzB42NjZAkafyfL4PB4PcvkO9xjY2NqK6uxqpVq/CVr3wFV6+K2/MQEU1153sHcKkv+DXkrTPysERQPeT75uuFXqVWjhNvsucIAriBEGbsjbk9qPrDGfzB2oULvQN44cJ1/NPxs7qPnwxEFXsL87MU56SJ2l+Go2Mw+A36NKNhvHXmooLgVpbXHMPjVXxK68jLCL7PRW/QRyPYG3G5he0cRWuPF9/Hv5JchWA6M9WINEGFkn3UhY7BEZy83qdrDQvy1L9/UXgWcrCncf8pzd/zu01RlZZCq9BbFOY+nlf4PeRLPGMv/hV7RQrfW74g0FVqc5pobo+4TlPUTjQUosefUsgLiCs4PRJwQ/D74Kxgtl52ago+tXx+iKucnBjsKbDZbH6XQwn6duzYAWAipNOrpqYGjY2NMJlMulp5+gZ/VqsVDQ0NmrfxzDPPCI8noiTX/pr69emCPzBTM4G5G+R/xqn/SRUiIiKaPL797W9j165dsNvt40Geb3DnG9Zp/fPlu72+vh4bN27EoUMaXQ+IiKagN9t7hds/uWyu8A123zfiL/QGv1lqNABLCycCQVGAEUorTtHsppPX+xRnc002nYOj6BBUzpXOMvnNYPMVrRmFooq92TmZ4/+PFc2oc0vS+P0hasMJBLfiBMSVaHrnCqq56hgWVo0uUpgRGA9KAZQv0c8IkP/GMQl+VvZRJ45euqHa+tLX/Dz1oEk0660vxFacWsGdnvtXVKVVpFCllZueivmCAE3pAwa+xMFe8lTsie7zZK3YG1MI9lwefe2clYiCPaV5kIDyHMmrDv//X7g9Ei4LqnrXzVH+HTvVTI/vMkQNDQ3jMxy8fvSjH+lurVleXg6TyTT+aVE9AVp1dTVqampgNptx6NAhXZV0lZWVqK2tHa8M/MY3vqEaCNbW1o634dy7dy/MZrOu74eIEkxyA9dPqO+TOTM+ayEiIiKKUF1dHZ599tnxIM83oCstLcXatWuxaNEilJSUID8/H4WFhcjPzx8/vqCgYPyDmA6HA319fXA4HGhtbUVLSwuamppgsVjGz7l79260tLTgm9/8Zty/VyKiRBGFO2lGA+4zz8KpjuBKMt834kWVMiX52X5vmOekB795Hkow9Z+nWoTbr9gGdb+ZHi6PJGHE5UZGSorinKdIKc3XWzM7X/FN54FoBXuC1nlzfObSKYVjV2yDWFyQo1yxJwr2BG/Q66no0nLVETxfD5Afh4myIC8LmalGYftHL7VAwZSZhu6ASrbXrvXgnRv6qvVmZ2dohlaigMk+6oTbI+l+rOtpxalm2OXGsCBwK8xSDnNuKcoNmqPWYhvCmNuD9BTluqRYt+KckZWBVKMBLoXQCwDSU4zIThPfpqjKNVln7ImqLAH58ROuYadbeB8pBaGAuGIPAK4FfBCk1TGEUcEM2eU6Kmunimkf7FksFjQ2NqKvrw92ux2tra3C2Q51dXU4fvw41qxZA7PZjMLCQqxZswbl5eXC827ZsgV1dXWoqalBXV0dtm/fjrKysvEwzeFwjFfZ1dfXw263Y/v27SHP5Tt69Cjuuece2O12WK1W3H///Thw4EDQvL2amhpUV1cDkANBb1UhEU0CPc3AmEbV8IKy+KyFiIiIKAIOhwN79uwZD/QA+fXJ1q1bsWbNGt3n8Q36lNTX1+PZZ5+FxWJBTU0N1q9fj/vvvz/stRMRTSa9Cu3PslJThG3QvG/GS5KEawGVEUBwtZKoFafeir0xtwc9Cm8idwraSEbTW+292Pf6edhGnFhkysbX716OFTOi/0bw+4IWcQCwdrZJsSJuIISKRyUeSRLOxJqdPRHsLVZoZ+mds9c/Jn4jP09QfSQK9hyjrpCCJJHAak4vrRlzsZRiNGBZYS5Oq8zZU6rYA8TVW4A4nBJRqmTyJXpuS5DDuhkq7Q99abfiDK9Vp1qYc0tRLl5p7fbb5pYkXLENqj4/R9yxbcWZYjRgTk6m4uMRkNtwilrFA8FzKQH599+Iyx3VADIalH4nR9JaV6kNrForzvl5WTAAQVWsgfeBUqtWPZW1U8W0D/a8VXV6WK1Wv7l5FRUVisHeF77wBdTV1QEA7HY7ampqUFNTI9y3rKwMVVVVQWGcHiaTCSdOnMDjjz+OI0eOwGq1YvPmzTCbzVizZg0cDgeamprGKxAPHDgQNJ+PiBLIPQacex7oOQNkzQBu/f/k+Xi+2l9VPj4tF1hcASzm85qIiIiSn/c1EiC/ntq/f7+ukC4cW7ZswZYtW1BbW4tvfOMb2LVrF1pbW2NyW0REyUbUCs/7xrrSrCvbyBi6h8aEbQED3ywVVajordhr6hRXswGIaSvOYacb/3j8/fEgpcU+hP2vn8eB+z8Q9cq9Fntwi7iizDTMycmERxJ/j9Foxdk7PCasLprtU7E3KzsD2akpGAqoqmrxBnshteIUB0n2Uafqm/daROFymtHg930kwi1F6sGeasWeQrCnV7FCJZMvpRaHfSNjUQz21CsyexVafyq14gTU5+wpBXuSJGFUVLGXEt3AbF6uerAnmjPplS+o2APk8DvZgr1epWAvgoo90QdMAPVWnOkpRszJycCNgKpzbytOl8eD870DOKnQbno5g73po7KyEpWVlVE/b2lpKZqbm1FXV4eGhga0trbCZrPBbrfDbDajpKQE5eXlqKioiLglpslkwsGDB2GxWPCjH/0Ix48fh81mw5EjR2A2m7F27Vps2bKFVXpEyejd7wJXX564fONNYNO3gYJl8mVJEs/XS8sFPvljwDjtf40TERHRJPKrX/0KBoMBFRUVeO655+Jymzt27IDD4cCTTz6JQ4cO4aGHHorL7RIRJZKorZr3zVSlN9h7h526qyBEAcaQy62rUuuNNvEbskBsg72T1/uCqqOs9iE0ddpx29yCqN6WNyTztahAboGZK2hjCkSnFaeoBSsAzMmZaG9qMBhgNmXj/R7/qsKWm/OqlFpxikKKIoX2ir0jYxEFe1cFQUpxXhaMCpVR8aJVDSSqavSKNNhTalHoS6kqrm94DCjUdzuarTg1rleq0tJqxSmiNmdv1O0Rfggh2oGZVmtgtcd5vsJ97hhzJjyk9uXyeBTv18gq9sTHqlVvAsCC/OygYO9a/zAu9g7gm384rbimmVnpKIzg985kw3eEY8hkMsUsOBQpLS0NuZUnESXQqB249nv/bc5B4NVvAWVPAfmLgP5WYLg7+Nh5dzPUIyIioknHWzG3f//+uN5uZWUlnn32WbzwwgsM9ogoIdweCae77OgbcaJ0tkl39Uw4PJIkfJPW+wa00hvsfSNjuNAb3ELSaACWFmq34gTklp5qVUuSJOH1th7F66MV7HUPjeJcTz/yM9JQOtsEAHj1muC1NeTAL5rBXt/ImPCN58U3Z9tlpyrM2HNGHux1KLQyDQwRFhXkBAV71wdGMOxyw6HUilPQVlCpWqlv2Kk7SPJyeTx494YNwy43LvcFVzzqaUUZax+YW6g6c23tzceaiFL1lq80owFOhXPraUOqFJiE8rzSqsjTCnoUgz2VMCcvPQ3zcjOD1in6feQ1IpjjB0S3FSegHeypVZ/lK/wudERQBRcLSlWWgBz0uzwepBpD+7k2dzvws7NtwuvUfmaAHGKfvO4/e7J3eAxPqIR6wPRqwwkw2CMiShz7FUAS9FIfcwDHq4CyvYDtovjYeXfHdm1EREREMWC327F27Vrk5cV/sP3atWv9RisQEcXLmNuDv29oxls336jMSUvBP963WjUEiMTAmEsYPHhbcCpW9YyMCSv2FuZnIyugCiZH0IrTe9tqwd7FvsGgSgxfNwZGIEmS4swqPd6+3od/Pn52PCi7r2Qmqu5dKQymAOXZUuGyCqr1AGDRzdl2KUYDstNSMBQwUy8qFXuC+XoAMCcw2DMFz9mTIFftiVpxGg3i9qtK1Z9K4Y6S/jEnnvj9GZztUQ5yFuSJZwPG04ysdDy8bhEOvHsl6LqPLJqF2+YUKB6r1ALXK8VgwHP3fwAPH3lbeL2eYHNGtvj+eL75Gj6+ZI6uajbNVpxaFXsKIZFWmHNLUW5QsNdiH8KY24P0lOBQSWk2YeDvqkhpVuyptuIUf89K7W4TRakNp5d91BXSh1H+31sX8cKF64rXa1XzKrWdVaoA9JpuwV50I2wiItKv/6rydaN9wKtPKM/XK1oZmzURERERxZDJZEJBQUFCbjs/Pz9pZuxZrVbU1NRg48aNqK+vT/RyiCjGfnymdTzUA4BBpxv/LggGokUpVPG+Aa30Bnt7/4hwttkthcFvliqFd4MqVWdnuhx44g+nFa8HgBG3J6LWb5Ik4T/eu+JX/fZKazdOXrdBKSvsVgjDwuVtaRnI7BOm5QoqHqMR7HUIQlMDgJlZ/sHe0sIc4fHneweErThz01OFbTDVqj9DcexSh2qoByRHxR4A/PGtC/CDLbdj993L8dcbluGxu27Bs5vXY/c9K1Tb0CqFPF4fWzxb9Xucna0eMAFyK0JRy87OoVE88D+voe50K6yC+Y+APOPxF+fa0a5R3ecYdcKtUFUIiOeqGQ3a378olHF5JFxReD7Fq2LvtjkFyBAEi153zlcuTVX6nu1JVrHXM6z+O9C3itMjSWi82o3/brLirfY+SJL/Y+FcT79qqJdiMKh++AMI/7m+vCj+HxxMJFbsERElSr/GG0vD3eI2nBmFQGZRbNZEREREFEMlJSWw2WwJue3Tp08jPz8/Ibfta+fOnThy5Eiil0FEcdLeP4y608Ef6jzb049Bp0uxpWUklNqqeSv18jPSYDQAge/NWzrtwplVojfcldY96BS/2X6upx+Pv9yk2GbQ1/WBkbDnJDlGXbgoaOP4fPNVzMoWz7RqdYgr7MIlmq8HBAR76alB1XXRaMXZJQj2irLSkRYQTCwThLWAPNNMVL2jVO2oPNMttODi7YC2eyJ6ZszFy8L8bCzMD62CsEBHsGU0GFA6Ox+WToffdYtM2ZqzKwF5fuKfrFmIfSfOC6//YZMVdadb8c8fXI0PzJ0IpGwjY/iLF9/RFap7JLnCUqkNq+gcBZnpmvMRRR8gAOT2sitmBAc2ShV70Z6xl5OeisfvXo69J84F/f7avmYhVgrW5qXUftWRbBV7IbRf3XfiPF5u6Ry//NDqhfizdYvGL7/ZrjxDFZA/WKL1WNDTdlZkulXsMdgjIkoUtYo9NQVLorsOIiIiojgpLS3FoUOH0N/fH9d2nKdPn4bVaoXZbI7bbYrU19cHhXrJEDYSUezUvH1Z8bq2/uGYVBgozri6WV1lNBhQmJke1ILynKANJwBhgJGTrtyKU+TQmau6Qj1ADvZWzQrvd6NSW81TnXbcNV/8Adm+ESfsI06YNFoF6iUK9ubmZCDbJwwVVawMjolD0VB0DAVXW80WBJo56akozstCW79/heaF3gHhfRjYytMrPcWI3LTUoFBSKygI9PYNm+Y+4b7Znyz0Vqzdv2RuULD30cWzdd/ORxfNxosXb+B0l0N4vdMj4T/ebUHN/RPB3g+brCFVytpGlIM9UTBcpOO5ZVL4+SgFeCNupYq96AZ7AHCfeRY2FBfhUu8AxjweGA0GLCnI0bxP8xUCcaU5luGSJAm/t3bhdJcDM7LS8ZkV8/1+32jpGdIX7Fntg36hHgD85P1r+NytxePh/7sdNtVzqc1a9JqVnaE6b1JkRla6ZovPqYatOImIEiXcYM+0NLrrICIiIoqTLVu2QJIkPPnkk3G7TYfDgV27dsFgMKCsrCxutxvIbrdj9+7dQdsLC5VbOBHR5PZmey/eUKleELW9jAalOUS+b6hqzbvyJZoxpVyxJw72Lgmq6JQEztkKhVpLuX6VN9NbFNoThkqSJGErzkUm/9aXomAvVq04lUI5UXXLZdugcE6f2pwxUTvOUFpxBrbyE8lLT9UMUZKdUnAFyK0qlxTIj5EPL5qNz9+6AKk3K/Qqls3Fp5bP1307BoMBj9yxFGoFfhf6BtAxKD/P3B4Jr17t0X1+QH3Onuj3j54wRymQG1ZouRmvVpxeWakpWDPbhA/MLcT6OQW6Ho8pRoNwHmm0K/Z+2GTFt187hxcuXMcPm6z465eaVNulBurRqti7eX8fu9QRdJ3LI+HNtomK2/Z+9d/fSu17fRkNBsU5e0qWT7NqPYDBHhFRYowNyHP0gugYEF6wLOrLISIiIoqH8vJylJSUoLa2Ft/+9rdjfnvHjx/H/fffD6vVCgDYsWNHzG9TSXV1Nex2e9B2VuwRTV3HLt1QvT5WwZ6oYgaYmLEH6HujHZBfoYqCIcVgT6HqzDaqP+i5PhhJsKd8O1dVft5K7TPDuX1RO1JzgX/Vo+jN/khbcQ6OuTAkuO3ZOeJQLpQ3wlWDPcFjSSlc7hgcwb+9eQGPv9yEH5xqwYjLrWveWDK14QyXUltGQA5+M24GWylGA/7itsX41ec24ti2TfirDbeEXIW2tDAXn11ZrLrP623yhw7O9fSrBnUiStV9kiQJQ109rXWVAjmlAE+pki8jJfoVe5EQBYCOKM7YG3G58Ytz7X7bLtsG0dDapfsceiv2lH6Heit0e4fHVH8HA/7/H1IT6py9Ty6bG9L+UwGDPSKiRFCq1lu0WftYVuwRERHRJHbgwAFIkoSamhqsXr0azz33HK5eDbOTgcDp06fx3HPP4d5778VDDz0Eq9UKg8GAiooKrFmzJmq3EwqLxYK6ujps37494e1AiSh+rtjUw6Jr/bGq2At+YzUzxYgsnzBJ7wy7WdkZQfPZAOVWnKKKvRGXW/gm/GdXFgurMq4PhP9zUXtTuV+lIk5UZRcOpftcT8XekNMdUpVNIFG1HgDMDqFiT4l6sKevYm/E5cY3f38aRy7ewHsddhw6cxX7TpxXXLevUN/kT0apRqNiFZ3ovkhLMeqaq6fkz9cvxrZVC8cr/wJ5g73XNWaiiSgFe8MuN0bdwc91Pa04sxTCS8VWnHGu2AtXvuC5rva7KFTX+ocxJPhZ/Oh0q+5zaM7Yu/nBDI9Cda33EXZBoZ2zL70htbeCVckthbkoykrH/NxM/J87lym2Wp7KOGOPiCgRlIK9hR8EOt8FhhQ+2ZmaDeTMidmyiIiIiGKttLQUTz31FL7xjW/A4XCguroa1dXVyM/Px7p161BSUgKTyQSz2YyCgoLx4/Lz8+FwTMyLsdlscDgcaGlpgc1mg8ViQWvrxJsYvq298vPzsX///rh8fyKPP/44TCYT9u3bh40bNyZsHUQUP5IkoVvQ0tBX7II9QSu8gCBPzxvtgHKgo1SxJ2onqVSRNePmm7KBc95uRNCKU6laUYs1ShV7Si09FwVU7OUq/PyGXK7xWVWh6hTM1wNUWnEWhhLsKQdrooo9x6gLLo8HqcaJkOXXlzuCKn4ar3Zj5QztOZNTIdgDAKXcNpSQVS+jwYCH1y/Cw+sX4dFfv4ezPf1+15/qsGHY6cbrbaG14QQAm0IQ1Dus3QZYSfRacSZ/xd71geGg50e4lKrt1CqUATmkO3rpBn5xrl2zYllr/qI3fD3f26+6HwDMz1P+kICvjy2eg+ebrwmD4lUz8/Cdj62D0RB+8D0VMNgjIoqmkV7ANQxkzwaMKn+MKwV7eQuB+RuBi/8rvt60BDAk16ePiIiIiEK1Y8cOlJSU4Mtf/vJ4WGe329HY2Bj2OX2DPIPBAIPBAEmSYDabcfToUeTlab9xGAu1tbWwWCw4cOCA8Hrf8JKIpo4BpwsjgjckfbU5hiFJEgxRfnNS2AovIMjT24pTKdhLTzEiOzUlqFJEVDGn9KZwQUYa5grO3z00hjG3B+mCSkEtWm3glLTYh6JyX4gCQqMBKMkPCPYEVTwAMDDmDjvYU6zYy1YIZ9NTMT83E+06gtRQZ+wB8v0+M1sOFSVJwo8sVuF+L10JntsVaCq04lQTi2DP193FRUHBntMjBztalcUivQrPaaXAT89MzxSjAWlGA5wB6WeorTiTLdgT/Y6zj7rwelsvNi2c6bddkiQ4PVJIv/u6VeaK9g6PoUhQnT3scmP/iXNo1Dlb0fs73K1Qsef98Iaeij2tSjyvubmZ+NePrsWPz1xFi20QEoA0owFrZpnwp+vM0z7UA9iKk4goepr/Gzj2JeClvwBe/irgUCl77xdcl5YHpJvkYE8J5+sRERHRFFFeXo7m5mY88cQTMJlMAPzDOe9lrX9e3jDP97iqqiq8+uqrCZtjZ7fbsWfPHpSVlWHLli0JWQMRJUa3xswiABhyuRXfII9EnyDcCgzy9LzRDojflPYStXgUhUuKwV5mmjAwkiDPYgtHuBV7/WOusENBX62CYG9+blbQG/U5isFe+C36OhWCPaWKPUDfnL289FTFIBJQnpnl297vbE8/7KPi703PfMMFedma+0wGSgHpUp1hR7juKha3KfzeO5fDOl+PQpik1NJRb+tfUSg32Vtx3rtghnD7ixcnOnVJkoQfnGrBH/30dXz2Zyfw3bcu6m7L26VSGW7pCp7t7Bh14mu/bdId6gETv8OVWojqDfZuKcxF6WyT7ttdMSMP/1C+Cv+19U7899Y78f0td+Cv77oFBTo/mDLVJdcjnYhosup8Dzh3GJBu/k9u4BpgEX8qW75eULGXtxAwGICilcrHFXC+HhEREU0tlZWVOHPmDA4cOIDt27cjPz8/KLRT4xvy5efno6KiAgcOHMC1a9fwla98JcarV/f4448DAPbu3ZvQdRBR/Km92errmiM6LSABwO2RcH1gRBgWBlZt6H2jXa1SSxTsdQnaQYoqCAGgIDNd8fzt/WEGexqzotREox2n6HsVtZ7LTRNXFUUS7Ilav2anpiiGiABwS5F2NbtauAsoV3/2+bRl9A0xQmXKSJ0yrTjvXzo3aFvp7HxkxLjKbGlBDmZl6w9DNi+Zgw+WzMTX714ubJWqFKBH0ooTEM/ZC7liLyW5KvZum1uAuTnBz6G3rvfhFWsXBsdcONR8FYfOXJUrvV0e/OrCdTz/vr7502ofIjnd6Qja9uzJS7oq63x5Z+wpfUjDPupE3/AYugWPizSjAR80z8KONSX414+uZaVdFLEVJxGRHrZLwJkfACN9wMxSYPWfAqk+/2O+8LPgYzrfBZxDQFrAJ8vco8CgoNVE3kL5vwYjsLgCuHIkeB/TkvC/ByIiIqIkVlFRgYqKCuzduxcOhwOtra04deoUHA4H+vr6YLf7f+rYZDKhsLDQbzZfoirzRBoaGnDkyBFUVVXBbDYnejlEFGda8/W82vqHsW5OQcS3194/jL9vbFZsqxftVpwAMDs7ONjrGR4Lmh1lU5ixZ8pIg1HhPd53bvQpVhkpkSQp7Io9QJ5Jdfu8wrCPB8RvfIuqSxRbcTrDD/a6BFVUMzXCHD0tINUeA4ByK05vyDkw5sIfrF2at6Nk+5qSsNqyJqOPL5mDIxevj1e2phkN+NO1i2J+uwaDAXfNL0K9joB15Yw8fO3u5eOXT1wLruxSqm6NpBUnIK62U5yx5w7enmY0IEXpl0qCGA0GfHLZXPzgVEvQdf/86lnF4376fhv+eOUCzcd+KBV7bo+E44L7U8uIy4MRl1vx/rWPOnFeISz8P3cuw2ZBoE2RY7BHRKTFOQgc/ybgvPk/KUcLMOYA7twjX3aPAZ1vi48d7gTSFvlvG2iD3FwkgDfYAwDzx4ArL/rvl7sAyC8J73sgIiIimkTy8/OxZs0arFmzJtFLCds3vvENmM1mVFZWJnopRJQAXTpacQLAtf5hXfsNOV241DeIjFQjbinMDZoF9713LqvOygoM8ooUwphA83OVK6XmCKpQPJL8vfuGQWqtOAsy05CRYsRowDzCl6504uH1i0MKdPrHXEHzuUIRSbUcIAcQormKBRnBP2ulYG8wooq94MfcTEH46mv5jFwYDfL9pkQr2FNrxdk1NIq/feWM5rxJkS+WmlE6Oz8qwXeymJGVjufu/wB+fbkDw043PrJ4tubPN1o2LpihK9i7OyBQF81os4044fZIQSGaqFo41WhAnkrVqK9IW3Em23w9r08smYP/arIqzqgT6R9z4fjVbnx40WzV/dQ+RHK5bxADY67x3zdW+xDGwnguAsD1gRHF36+OUadiFeByHVXBFJ6p8XEHIqJYuvq7iVDPq+04MHZz8HDnu8rHDnUGb+u/Jt7XN9grXA7cugMw3PyjJKMQWP/IxGUiIiIiSlo1NTWwWq146qmnEr0UIkqQboUZVIGuOrSDvVb7EHa9+A4e+20TvnrsPfzNK81+85ecbg/ebO9VPUdgxUxuWirSNCpbslNTkJ+h/Ia8qBUnAHQGzMcTBXvZaSlITzEiPcWI8pKZQdf3j7nQ2Nqtur5Akc7IG4ygWg5QDzADKVbshRnsSZIkfIN/lkawl5OWitJZ6jOvtIIn0fcHAOd6BvDor9/Dxb5B1eNFPruiGDtKS6ZUqOeVm56Kz66Uv794hXoA8IG5hZrtOI0G4GOL5/htmyEI9iSI286KthVmpgV9EEFJpK04kzXYK8pKxz0LQqtABvS1sBW1v/SSAJzpmmjHeb63P+Q1eF2xKT+P7aMuXLYFB3vpKUaYTVNjPmYyYrBHRKTlzH8Fb5PcwEC7/HX7a8rHioK9EYWy99z5/pdXPgR8/AfAB/8N+NhBYNZaXcslIiIiosSxWq2orq5GRUUFysvLE70cIkoQtblHvtp0VOz9sKkFNwYnQps32nvx07PX8GZ7L355vh3v3LCpVlwBwTP1DAaDsEWkr7m5mapvyCsHe/4Bk13QitO3iq1i2TzheY5cuq66vkCRBnuRtMEElNsQCoO9tOi24rSPOoXVNFqtOIHgCq1AWuFTqtEoDIBfu9YT9n2i9Nii8KUYDdi8RL0l4oZ5RUE/e1GwB4ifb32CbXrb/gJKFXtKwZ6oYi95o45PLZ+vvVOAU5121Tmsg04Xhpzin4/XaZ92nKHO1vPVojKDdMztwfvdwaHhIlN20rVGnUrYipOISItL4X9eg9cBjwtofUn52CFBH/mRPvG+mYI/prNnyf+IiIiIpoHjx4+jtbUVVqsVFosFAGCz2cbn65lMJhQUFACQ23WuX78eJSUlKCkpSZq2nXv2yO3a9+/fH5fba25uDmn/4uJiFBcXx2g1ROSld8Zee/+IsKWdr3dv2IO2/cd7LSGtR/TmemFmmup8Jq1AR9SKE8D4/DAvUeDlGyqumpmHRabsoDeOLZ0OtNqHUKKz4iPiir0x9TfItShW7GUE/+yz0lJgQPCQjnDXoBQka1XsAXKwd+DdK4rX66kqK8xMh2M0smDUF4O92Ni8dA7qzrQqfhDgk8uCg79Qgj3RPM3Qgr3gYE4x2BO0lEzWij0AWD+nAJ+7tRg/eb8tpONevHQDO29bIrxOzwdIWnxaNIuCvRlZ6ShbOBO/ON+ueh6rSrAHiCsHFxXkaK5vKmpra0Nbm/77OdS/5b0Y7BERqXGrvBg7uU/7+GFRxZ6gRUpqlvyPiIiIaJr58Y9/jPr6ejQ2NgqvlwLmkfhWjxw5csTvurKyMpSXl2P79u3Iy4v/TA/v97F3716YTOqtzaKlqqoqpP0fe+wxfO1rX4vRaojISy0w8+WWJFwfGMGCfPHrQY8kRVxJBgS34gSUZ6N56ZmtlmIwBM2N6hwKDPZEb/ZPrMdgMOCTy+ai5u3LQfv9+nIH/uK2xarr8OpNcCtOUWUiIP7ZGw0G5KSlBt234bbiVGr9+v+zd99hTd7rG8DvDDYkARkqQnAvQK11VAG7Hainaoer41fbOmp3a4c9PT2ndXWdTldPt7PL1t1twW2tMsStDCcqkLBnfn8gkZDvmwFJWPfnurwqb97xpSKS936f5wn0sh6QdVB5o4Ofl+S8R1vCQX9PN2SY589CE7qH4rujlm96S4XG1DDBPp4Y0C4AewSte/3clRjY3vyBc9GMPUD8900n+Lsu1apVRNSKs9iuGXtNt2IPAB7p1wmjOrdFuq4I+87lYvNJ6602fz2djYf7dhRWT9vyAEnN9+PKKgNOCdppdgvwxaPXd8b4HqHILiyBv6c7pm3ab7ZfuoVWnFIiWmkbzjVr1uCdd95x+nWa9lc7EVFj02c07HhhK05BsOfh37DrEBERETUzmzdvRu/evfH8888jMTERBoPB+Ks2mUxm8quu2sclJiZi3rx56NWrF2bOnImsrCxXfToAgDlz5iAqKgpTp0516XWJqGkpKq9AoaA9WluJsOJ8gXQ7zmKJahV7iSpZgqxURVkL9hRymXBm18VaM/YMBoOwikftYXqz/5aIYLgrzG9T7j4rMcpC4IqNcw2lWGtpZ409M/YAwNfd/M+kviHuJYnKHVtacQLAIAvtOJVy67ePba3KeqRfR9zeKcTqfsE2hIlUP6KqPKC6VaSoctjWir2SikphFV3dv+uWSLXirPuzYfV2QcWeoulW7NXooPJGTFggnhrUFR+Pug5RQSoAgEKi7XFuSbnZwxI1bHmApOb7caa+CKWCP5+uAb4Aqr/f9wnRoK3E9/1zBSXC7ZZEqFtnxZ6rMNgjIrIk72TDjhcFe6WCVpyeDPaIiIio9ViwYAGmT58OnU5nvFlTO7irHdZZ+1Vb7e0bN27EkCFDsHr1apd8TnPmzIFOp3NZC04iarqk2qNFh4grec9buGFa3MCwCRBXwQDADaFtLB5nSwtGUThY+2ZzYXklKgR9/+qGXSoPN+Gst0x9Mf6TmIZXE9Kw9eQFlNe6MX0sJx8Ldx7F/B1HkJqta3DFXn2q5UoqKrHqUCZe334YnySlC/eRCjZ83M0bqdW7Yk/iBr8t1XaA9Jw9UbWhiLXqzxq9A1XoqPGG1kIlj6dCPLOPHGNQ+wB01Jj+//d1U2J0V/GsS5WHmzB0qvv3TaoVqz1/lqKKOwOqZ7jVJa7Ya/rBXm0RGh+8c1sfrLpjIDbcPQQvDuku3K92O83abGnFWVheicKyChyTmK/XLcC0w4W7Qi45A9ReEZrWWbHnKvwuSURkia6BwV5JDlBVDshr/TAsmrEnmq9HRERE1AKtXLkSH330kTHIqx3QRUVFITo6GhEREQgPD4dKpYK/vz9UKpXxeI1Gg7y8PACAXq9Hbm4u9Ho9MjMzkZ6ejuTkZKSkpBjPOWfOHKSnp+PFF1902ueUkpKClStXYsqUKYiKinLadURqKhRtxfl6RM4n1RYxKkiFn09dNNtuKdgTVf7ZKzJIJdw+oL0/7o/S4osUcacaW2bbhXh7IgV6k23ZhaUwGAyQyWTC+XqAuIptcGgAEjIvm21PzKqu2ttx5grO5hdjWt+OOJaTj9lbDxpn1P2RIZhvb6f6tOKct/0IdgvaGtbwcVPATVCJCEB487y+wZ6ocsddIYefIDwUiQxSwVfQGnRYeJBNx2u8bAsAtWpvyGQyPNKvI+ZuOyTcx8ddKazQJ8dQyGV4JaYn3tl7HIcu6RHq54XZ13eWrMyTy2QI8HI3+xqrW7En1Yq2oRV7QHXlsked15pjK04pNQF8R4mZdOm6QmFVrdS/NXVlF5XieE6+8LWair3a/L3cGtwC2sdNgUCJr6mWbuLEiYiNjbV5/7S0NLtb6wMM9oiILMs70cATGIDdrwNtegGdxwJyJVCmN9+NFXtERETUCuj1ejz//PPGQA8AZs2ahbFjxyIyMtLm89QO+qRs3LgRH330EVJSUrB48WL07dsXI0eOrPfaLXnuueegVqvxxhs2zGB2sF69emHgwIEuvy4RSZOqotCqvaHyUJpVtlgK9oocMF/vto7Bkq9NjQpHO19PvL3nGMprVdZd11Zj05yzEEHFXmllFfJKy+Hv6S5swwkAGsHN/oHtAiCXAYICP6OvD5/B+O6hWJWaBQu71UtNiHouvxgfHzyNy0VliApW4b4orTBwOJaTbzHUAwCNhUo2X0Hodq6gBJeKSm2utKsh+poL9HK3OSBTyuW4u1cHfFqn6nBEZ+ttMwHbKvaCvD2MVYoD2wdgSIc22HnGvNWqVLhBjtNB5Y13bu1j8/5tBMFe3Yo9RwR7UtXForabwlaczaxir64Ofl7CuaUNqdgDqh+2OC6o2Gvj5S6coejv6Y4svXSLaFtEaHxabUAfGhrqkgfpmmeMTUTkClWVgC7d9v3DbxVvv7gPSPsC+OMJoEjiKUIPVuwRERFRy7dy5Urj7+Pj45GWloaXXnrJrlDPVqNHj8aWLVuwcOFCGAwGTJ8+3eHXAIAVK1YgJSXFYaGeTqdzyHmIqPFIzT0K8vZAO0FYZjnYq3/FXvcAXzw9qCtuipAO9gDglo7BeP/2vrghNADhKi8M7xSCf8b0tOkawRJz+rILq/8f5ErOnTO/maz2dEPPQMsPblQZgN3ncrBDEAg1VFllFQrKKvDEL0nYnnUFR67k45vDZ/HOnuPC/X85JRi9UYfUfD0ACPXzEq5h2d+nbF/0VaLKHXvDwbt6hmJanwiEq7zQK9APi26ORGd/82oeEVHAW1dEnQrQGdd1Es5VrNsmkhqfqJqv7kxLncTfdbWN7VwB6Yq7urNGyyurzMIvQDoYbC7cFHJ0UJl/X8jQiYM9W2bsAdVzXE/mFpptF1XrAUCAHX9mUur+fSfHY8UeEZGUgjNAlY09+hUeQMRwIPNXy+c79o34NbbiJCIiolZg/fr1kMlkiI+Px9KlS11yzalTp0Kv12P+/PlYvXo1Jk2a5LBz63Q6zJ8/HwDqHRyK2mju3LkTWq22QWsjosYjmncml1VXQbTz9cTROpUTFwpKjK0r66pvsBffpS2eHNjV5v27BPjiP8N6232dYImqvv0XcrF4/ymkXRZ0rIF04DW4fQAOXRIfU2PNoSz7FmmHLScvIK9OQPFn5iU82r+zWUBx9Iq4tV1tosrEGjdqg/D14TNm2//MvIxRF3JxXVvbOvsYDAbhDf5AO4M9pVyOib3DMLF3mF3HAUDPQBU8FXKUCGah1ag7b6udryce6huBxfuvBZkeCjmGd7KtSpBcR1TVlVdajsoqAxTy6u9bzmzFWbftZt2g79rxzb+GSav2NgvyMvRFJv+va9jaivPARR1KBX83u0kEe5YqjW3FYM/5mv9XOxGRs9jThtMvDPC24YfPjF/E29mKk4iIiFqBzMxMAMCbb77p0uvOmjULarUaGzZscOh5n3vuOVbYEZEZUXu0Nl7uUMhlaOtrXo1RVFFp1p6zRn3mvgH2V2vVV7DEdT5LypAM9QALwZ5gjlRd5yxUODbUF8nm8warDMCJ3II62ww2taqzVLHXNcAXt0uEWP87kG713DWKyiuFbQkDvV0338pdIUe/thqL+0SozVtsjuseihdu6I5B7QNwa0Qw3r41GlrBftS4RBV7VQYgt9YMTecGe6Zf32ck/u7Zc62mShSIlVVWmVV260vLJf/dqCtN4mEJ0d9JQBzk2iuCLXWdjsEeEZGUnCO27+sXVl11J7NWCC3x9Bor9oiIiKgV0Ol0iIqKgp+fn8uvHR0djYwM8xu2RESOdklQRdHGqzoAa+crrnCTascpVbEXFx5ocQ32VmvVl1QrTktkAFTu4hvwWrU32tbjnFK6+Nt3c1lU1QKYzxNLzytCgQ2hq6VgDwAe6hsBHzfzMON4bgGyC20LMKXa8QV6ueZroMbg0DYWX69bsVfjlo7BeP3G3nh+SHd0b+P6nw/IOqmgJ8dKsKeQyYRf31JsbcWZrjNvKwm0jDBJ6nOo+ZwNBgO+TM7AhO9223xOqVmnUn+u/g5oxallxZ7TsRUnEZFIWT6Q9bvt+/uFATI54B0IFF6w/3qs2CMiIqJWQK1WQ6PRNMq1VSqVsWLQUZYvX273MUOGDDEJGNPS0qBWqwFUB595eXlsw0nUzF0RVOwFXa2ekg72itEj0DzUkAr2Ovv7ICHzsuQaglxUreWpVEDj4SZ541hE5aE0aylXQyaTIS5c3KKyPoZ2CMQJwWwpe9UNXlMu2VatrfGw/Ofg7+mOKZHhWH7gtNlr5wpKJFud1nZJ8PUGuO5roMYgC9WWMgDhKt7ob65EFXvA1e91V//Y9YLvAWoPpbDFsBSpGXl1W3GezhPPnGsJ7R87SnwOGboixIQB+87n4qtUx/w8KxXg+TewFafGw63B5yDrWLFHRCRycj1QYb2thpHv1R70XpaHkgvJFIC75QHhRERERC1BeHg48vLyGuXaqampUKma9s9carWaoR5RM1deWSUMuaxW7ElUZxVViKvCOmvEs5FquLJaK8jOCjtr85smR4ahrQ2BljXdA3wthk32qBvspWbbFuzZUvnSO1D8b1N2oW3zs6TmbLmqarNGGy93yZld/p5ukm0WqemzpWJP9H2v7lxKa2xtxSmq2AvwdIOqBbTibOfrBTfBgw/pedWf89aTFy0e38mOqkWp8M3fq2H/H6Wqc8mxGOwREdVVXgSc/NG+Y/yuBnve9Qj2PDTV1X5ERERELVxUVBRSUlKQn5/v0uumpqYiIyOj0aoFiaj10JeJg7iaG6VB3h5QCCpY7GnF6SaXIUxlPquvNlfOV7N3np/Gys13Hzcl/hd/HZ4d3A3PDOqK6GB1vdY1qks7dPCz/P/JVrWDV4PBgGSJmVV12RJsSLUzvSgIe1OzdUjIvITcq61BLxeVYs2hLOHxrpqzWNug9uIg1V3Bex7NmcWKvatE897snXknHeyZfh/M0JlX7LWENpwAoJDLEC6o2kvXFaG8sgr7z+dKHuvtpsDQDpZb4tbwUirgJdEmNaCB1XY9JR5WIMfid1UiorpObwbKC6zvV0OmAHzbV//ey7Z/QE2wDScRERG1EqNHj4bBYMD8+fNddk29Xo/p06dDJpMhNjbWZdclotZJ1I4OuDZTTiGXIUQQ5FywI9jzcVMi2McDEt0s4e2mgLeb66bvSN30lyJ1M7k2D6UCwzuFYETntvhHt3Z2r8lLqcBN2iCbrmWL2sHr+YISs5l7UqyFmEB1NZRS8IdZu2LPYDBg0c6jeOrXZLy2/Qge2PgXPj5wGg9u3I9zgq8dhUxmdb6fMwzuIA72boqox0PQ1GSoPdyEDySYzNgrMf/eZ28FnZcNM/bySsqQK7hWS2jDWSNCbR5SZumL8feFPBRViNszy2XArP6d0cHKQx81LFUTN/R7x+0d+ffdFRjsERHVlfmreLuqo3i7dzAgv/qmydPyAHMhT8e0BiEiIiJq6uLi4hAeHo4VK1ZgwYIFTr/e9u3bMXLkSONMu6lTpzr9mtbUbUXaWK1Jicg5JIM9j2tBW1tBO057Kva83RRQyuUIlqjIcnWllr3VHZ52Vm8NaBeAQDvDw5sjHBfqAUBOcZmxaujgxTybj7PlBrlcJhP+mWUXXQv2Dl3W49f0bOPHReWV+PrwGZPAo7Y2Xu6Q2zHbzFG6+vuis795KBEXXo97JdRkyGUyBAjaM9ZU7FUZDNCXiWbsOb4Vp6haDwC0gjCsuRK1sqw0GPDtEfHs0dFd2uKLMQMwvFMIQmxsY+xv4XuqUi43+TfLHn2C1ejAeZouwWCPiKi28iIgXzCENjAa6BAnPkZZ6x8sTRf7r8lgj4iIiFqRZcuWwWAwYPHixejduzeWLl2KrCxxG7H6SE1NxdKlSzF06FBMmjQJGRkZkMlkiI+PR2RkpMOuU186nelcJr3etnZuRNQ8SLXirF25Ipqzd6moFOWVVWbbi8rNz+d9NbASBYQA7A7BGqqNnW0/O/tbng9Yl5ebAk8P6iasapMyqktb4+9jw8w763jXY95bTWvMn05lW9mzmlwG+LnbFmyIQtqLtSr2fk+/ZNN5aoiqQl1BJpPhmUHdjF/jCpkMD0Rr0cXOP3NqetoI5nbWtKgtKKtAlcH8GHuDPQ+FHKK/5bVbcaZLBHstaa5bR4mQ8uBF89meKg8lZl/fxfjvgVRr37qszf+Umr9nTe3vveRcrqvLJ2okusISbNp3FL8dPIGPHx8HuZx5NlmgOyXeHjoUqJRotaGo9Y+mfzdA3RnQnbT9mh5sxUlEREStR1RUFBYuXIgXXngBer0e8+bNw7x586BSqdCnTx+Eh4dDrVZDq9WazMRTqVQmIVheXh70ej3S09ORl5eHlJQUZGZee0DLYDCYHPvmm2+65POzZMWKFWbbPvjgAyxfvrwRVkNEzpAvUbGnthLsVRmqK7RC68yEKxRW7FXfzmvr6wkIbvQGuDjYs7dib6ggaLNmQHt/vHFzFF5NTBPO8qqtb4ga3QL8jB/f3TMMiVlXTPYZpg3CnxmXJNvaiZwvKEGVAUi7bNsDGSoPNyhsDCNFN+OzC0tgMBggk8mQmHnZ5nUCQExY41XIdQ3wxaej+yNLXwyNh5vFyiBqPjr4eeHIFdMZyWf0xSirrILOhu97tpDJZPBUKswqUU2CvbxC4bHaFtSKU1T1KmVguwCT7zMBnu5QyGSoNAiS1lqsBXf+nm7IMP/nxarG/N7T2jDYoxYpJ78I63al4fsdh/Bb0kmUX/0HYNrw6zGkp7aRV0dNRsbPQNqXQGUpEBoHRD0E5J0Q76vuDMgkQuGI4dd+L5MBMfOBQ58D+nTAvytwajNgsPDGgxV7RERE1MpMnToV4eHhmDFjhjGs0+l0SExMrPc5awd5MpkMMpkMBoMBWq0WW7ZsgZ+fn4WjHS8lJQWJiYnIzc2FTqdDZmam8PPbtGkThgwZgpiYGKjVavj7+2PKlClQq9UuXS8ROYZU6OTnfu0WnCjYA6qDo7rBnlQrTgDwUYpv61m5n+twtlbs+bopMfv6zgirZ5u2qGA1PhzeFx/sO4l953PhrpDjwT4R8FLKsezv0yiqqESfYDXm3NDd5LgegX6Y1b8TPkvKQHFFJYZ0aINpfSLw1/lcu4O9/efzbN7fU2F7VaAo2CuvMiCvpBz+Xu7w93JDnkR4Uld8l7YYW4+5hI6klMvRUdNyWiOSdHvIs/nFKBRUFgOAuh7tHD2VcsvBnqBiL8THAz4unCvqbG283OHv6SacJVjX4FDTe4oKeXVr3wuF4vbONZxRsTehRyjc7Wy1TPXXcr7iiWrZczQLj7y/zmz7up1pDPaoWu4x4O93r32cvqW6pWaZ6HEUGaDuCCjcAZ+2QOGFay+5+QDth5ru7u4H9Hvs2sdVlcDpTdJr8WTFHhEREbU+cXFxSEtLw+LFi/HRRx9Bp9MZKxNqGGy4O12zf81/DQaD8bi5c+di5syZTli9dV999RVWrlwp+Xrt4C4vL89k3/j4eAZ7RM2UaM4UULcVp5dwH9GcvaIK8xvmPleDPT+Jm+a+7q693dfGhhvAE3uF4cE+WpPv8fXRztcL82+KRFF5BTyVCuMcuRGd26K4olLy5v647qEY07Ud5DKZ8RgfNwXsaXCZoSvCnxm2V85JhR0iUnOxLhaWwt/L3ebQ4tvxg6G2Ya4fkb0iJNpDPvtbsuQDDfX5WvRSKpAL0++jxVdn7BkMBqTnmQd7UmtrrmQyGboG+GLvuVyL+ylkMlzfzvyeYrCP9WDPWmW3vZXYvm5KjO/e3q5jqGEY7FGLdHOfzlB5e0Bfa9AwAKzbeQhvPDiiwT9IUguQ/pP5tqzfxG0xfTsAyqtvvPo/B+x/Cyg8X11p1/cxwM3K04ZdxgGntwAwn5cAgBV7RERE1KrNmjULs2bNwqZNm5CQkICNGzeazaGzpHb4p1arERMTg7FjxyI+Pt4Zy7XZG2+8gTfeeKNR10BErqcT3OD2VMpNqhikKvYu1An2DAaDRMVe9e28mLBAfJ6cYfb6MK1rW6GpPd0gl0E4Y6tGB5WXQ+/FeNcJuuQymdXwS1lnNIuPnQHophMXhNsDPN2QI6issS/YE8/Fyi4qQQ/4oUBidmNtbX08GeqR00jNsLPUGtfeVpwA4CmYf1lTsXeluAwFgr9XLakNZw1bgr2oYJXw+1hbX08kZ1v+WdpaRZ7Gy/Kf3YKbIvHTqYtIvpiHtr6eeLBPBIIlHlAg52CwRy2Sh5sS8QN6YPWfSSbbT1/MRdLp8+jbiU8QtHrpW8y3leZV/6pL0+na79v0BG5dBlQUV8/WU9jwBItve6BDHHBmm/h1VuwRERERIT4+HvHx8Vi0aBH0ej0yMzORlJQEvV5vbGdZW03bytqz+VQqVSOtnoioml7QLlHlbnqD1NddCT93JfLrhDXnC4pNPi6vMqBCkJbVtOLUqr3RN0SNg7Xm7HXUeKN3oGu/F8plMgR4uuNyscRcelSHX02Nr4Na9z3crxMW7Tpqtt2eKqJgb3Gwd7Gw+oF10deV2fUkghciRwj29oCXYP6dJfUL9sxbOSZl6/Dgxr8kw/uW+LVfe06olBtCxfNKtTa0O7bWitNSxZ5cBkQFqYTVguQ6DPaoxRo3pJdZsAdUt+NksNfK2TtwQN3Z9GO5srrdpj263y0d7ImqBImIiIhaMZVKhcjISERGRjb2UoiI7CKqXlEJWma29fVEfk6Byba6rTiLJCq+vGpVtLwa1wufJ2XgVF4hQv288FDfiEbpUtTGy3KwV595Tc5W09K0IcJUXrhJG4RlB04hr07V3jg72tJJVbpkF5bCYDAIK0HramntCKlpkclkiFB74/CVfJuPUTmoYg8AsvTFwu0A0MXf1+7rNHVdA6x/ToNCxR3AbAk6rX1PthT8Rah94CHx50Suw2mG1GKN6N8NnoJy5HU7DzXCaqhJKbVcym5G09n6PtaoIoB2N5hv925rW9UfERERERERNXn5ghl7opvbonac5wpKTNoLi9pwAqaBlI+bEo9e3xlv3xqNpwd1rdeNdEewNq/J38rrjcHeVpwiHTU+UMhluLtnB5PtbbzcMSw8yObzuCvkwhvp2UWlKCyvRKUNDyhHtMB2hNS0aO2ojPNWKkxaENvKy87AKEzl1SK/9gO93KGxEK6FqbwQ6iee12rL/w9rFXuWvmfbEjqS87Fij+pt2rRpcHc3/0v+yCOPYPr06Y2wIlM+nu4Yfl1X/Lj7sMn2Q5nZOHrmErp3sP0HLGomDAYAVYDMyg8B+Vn2nbduxV599X4AuJQEVNQa9NtxlGPOTURERCSwbNkyLF++3Gx7WZl0VQUREdWfuBWn+e03UbBXVF6J/LIKYzgnFezVnS/XFLSxcBNYBkDTSIGjJdZm8tmiZjbenT1C0d7XEzvOXIHG0w0Te4XBy86KwGBvD+TWqfq7WFhiVgkopSW2I6SmpaMdVaH1fchA1IrTklGd2zZKlbKzyWQydPX3xb7z4uKEwRLVegAQ5O0Bb6UCRRJtU33cFFYr7ixV9DHYaxqa3k8C1Gzk5OQItxcUFAi3N4ZxQ3qbBXsA8MOuNDx/17BGWBE5RfEVIOkj4PxuwM2vOkDrOFJ6f3uCPa8gwMNB8wn8woCY+cCJH4DyguoKvogRjjk3ERERkUBBQQEuXLjQ2MsgImoVqgwGs7l5AOBnY8UeUN2Os+aGeKFEK05vB7SQdDRLFXtqDzco5E3vxruPe8P/PwZ7V/85ymQyDA0LxNCwwPqfy8cTR+u0Z80uLLVpvp5cBoTZMFeLqCHsCY8tVZtZItWKU8RNLsNtHUPqdZ3moFuAdLA3qL10sCeTyRCh8UbaZXHbVFtaI1t6GIPBXtPAYI/qLSAgQFix5+vbdP5yjx7YA0qFHBWVVSbbv9megjl3xrXIJzpanZIcIPF5oPBc9cfl+cDBDwF1RyCgh/gYe4I9R7ThrM2/GzBgjmPPSURERCTB19cXbdu2NdteVlYm+aAeERHVT2F5BaoEHRPVdgR7FwpK0L1N9Ux36Yq9phfstfHykHytvjf4nc0RFXvBPtKftyPOlV9WgQuFJYK9TYX6edWr7SGRPbR2VezV7++XPcFeTFgg1E30+4sjWArQIoPUFo+NUPtIB3te1v+fWXoYo7OG8zybAgZ7VG+ffPIJBg4c2NjLsMjf1ws3RXfCLwdOmGw/cPI87nv7GxSVliNE44uX7rkRHQItf0OkJqhUB2x/6VqoZ2QAkpdWz7Ur1QNtBwIRtwOyqz/k2hPsOaoNJxEREVEjmD59urBN/t69ezFu3LhGWFHjSExMxMqVK7F06dLGXgoRtWD6UnGFnegGt6WKvRrSM/aa3u08S604rc3fayy+DghIQxwY7Emd63iO9c5Y7X3Fs7aIHCnAjhDNQ1G/v1/2tOKM72L+8FpL0jXAT7j9Jm2Q1SporYU5e7ZU7AFAuMoLmfpis+3W2niSa/BRDmrx7oyJFG5ftS0JP+xKw7ItezHoqcUoKC518cqoQQwGYO8CID9T/HruMSDjZ+DCbuDg+8DRNddesyvY69iwdRIRERFRo0tNTcWmTZsaexlE1MLpJFomqtzNb4YHeXtAdF/WJNiraD6tOC0Fe7beRHY1H8HswxrVrS2th2UhPuKAtj6kzmVLsOemYEcqcj6ZTAaljW11SyTmu1njZWNo1Enjg+jgll2kEeTtjm6Cqr3bOgZbPdZS29QAG78nxwhaC98XFW7TseR8DPaoxZs8rA+CrJSKX8gtwKc/73fRisgh8rOAy8m273/sG6CiBCgvBEqu2H6cH//BIiIiImrucnPF80mIiBwp346KPaVcbpzPVlvtYK9QshVn06vYs1SV599EW+VZqnzUqr2ttpvzVirg48CQNdhbXLGXdllv9dgbtUEOWweRJSM721Yl16+tpl7nt6UVp8bTDS8N7dHiRyzJZDI8en1nkwcn/tGtHQZYmK9Xo6OFe+G2tke+o3t7RAapjB9HBaswoUeoTceS8zW9nwSIHMzb0x1P/mMo5n75s8X9fk8+icf/McRFq6IGy8+wb//KUiD7b8DT+j9+RjIl4NPOvusQERERUZOTkWHnz45E1OzkFpdh44nzAIBbIoLR3s/1rQn1UhV7ghl7QHU7zrrz02xpxdkUK/ZEcwRrNNVWnJZCua4BfpJBW41gHw+HBguhEhWC5aLBjbX4uSsxsJ0d9zqIGuDmiCBsOH7e6n5DOrSp1/ktBXveSgX+M6wXegepoJS3jnqlXoEqfDK6PzLyiuDnoUSYSroSrzZL4Z2t35P9Pd3x1i3ROJlXAIMB6Bbg2+LD1OakdfwNoFZvZvwgqK20R9h+KANVVVUuWhE1WIH1HyLMnNsBXE6xfX/f9oC86b1hIiIiIiL7pKamNvYSiMiJzheU4P82/oUvUzLxZUomZmw5gMM2VDk5mr7M9oo9QDxnL7uoBBVX700UlZufTy4DPBVN73aepXlPtlaHuJqvhVacXf190cFKOOzINpxAdQtCqdmLUgI83fD8Dd3h1QTDXmqZIoPUeCBaC8XVgKdXoB/m39gbna5WuLrJZXhqYBeE1vPhCi8LM/b6t/NHnxBNqwn1avi4KdErSGVzqAfAYgBnTxW1Qi5DtwA/dG/jx1CviWHFHrUKah9PzB49GPPWbpPcJ7egGIcysxEV0bIHr7YYhfUI9rL+sG9/vzD7r0FEREREQvn5+cjLy3PpNXU6HVasWIGMjAzejCBqwb45fMakbWVxRSW+P3oOcwNVFo5yPMmKPcGMPUAc7FUZgEtFpWjn6yWs2PNWKpvd9zNb5zm5mqVWnN0CfC2GlUB1xZ6jadXeJlWbUiLU3njj5ih4uyngYeNMMiJHmRIZjvHdQ1FQXoFAL3fIZDJc384fl4rKoPF0g3sDHj6wVLFny9xLusbf0w25Jeb/Lll6qIGaD/4pUqvx+NghWL51Hy7pCiX32X4oncFec1GfYM9eDPaIiIiIHGbw4MHQ611fQUNELZ+oLdy2jEuYO7SHS9chCvYUMplk60yp6qzzBSWSwV5zrMzyb6LBnqeFyqBO/j6otNIC0xnBXoTaB7vP5ljdL8jbA/5NtMUptQ5ebgqT70cymcwhfycsBXvWqmjJ1IN9IvD2nuMm2+QyIMzP9so/arpaV90qtWqBah98//IU9A4PltwnITXddQsi2xiqgMzfgO0vAXvmA7pT1dsZ7BERERE1K5MnT4bBYGiUX0TUcpVVSo/UcPXff1ErTj8P6Qq7thaCPUA8Y8/SXLjG5iFRpWNP2zdXslT56KlUwMddaXHtIVZm8NVHR41tN9wtzTQkas68LAR7UnMoSWyYNgiaOt8rbghtA3UT/Z5M9mGwR63KkJ5aJH30OIrW/RvRgsq8xEPpfOPf1KRvBfa/DVw6CJzbDmx7EtBnAMWXnX9tv3DnX4OIiIiolXjssccAVN9IdfUvImq5zuYXS75WXGEejDmTqGLPUgBjqWIPEM/Yk6r+awqm9Y0w26byUDbpm8i9g8zbtcZ3uXa/yFKFULCDZ+wB1RV7tmCwRy2VpUpaVuzZx0upwJu3RGFAO3900vhgTNd2eP6G7o29LHIQtuKkVkcmk8HDTYmY3lokp18wee18Tj5OXchB53ZtGml1ZMJQCaR9ZbqtqgJI/RSAIIDtMgE4vwsoPOeAi8sA31AHnIeIiIiIAEClUmHy5MlYtWoVZDIZVCoVNBoNVCrnzMDS6/XIy8uDTqdzyvmJqGlIz5Met5FTXA5vC3PUHE1fah7EqSzMMvJzV8LHTWEyHxAAzl0NK4sEwaQrPx97DQsPworUTJP/D6O7tIO8CT9gMbpLOxy6dK1NtEImw909Oxg/DlV5IeWSuI10iBNacXZQeUEuq561aElTDkuJGsJSK04VA227RWh8MP+myMZeBjlB0/1pgMjJYiM7YvGmPWbbE1LTGew1FVfSgDLBjZiL+8T7B/QAet0LZB+obuEpVwC7XhXv2/v/AHVH4OBioOiC+evewYDS8U/fEREREbVms2fPxqpVqwAAW7duRViY81ufJyQkYPLkyU6/DhE1jnRdkeRreSVl6ODC1m2iij1LN6JlMhna+3rheG6Byfaz+dUVe3UDP6BpV+wFeLnj3dv64OvDZ3CpsBSDQwPwj27tG3tZFt3aMRjebgr8np4NmUyGSb3C0L5WVZClCiFnzA50V8gR6ueFLL10JSoAqD14S5daJkutOInoGrbipFYrtrdWuH3TvqMoKSvHJV0hyl3ctqPVqygBSmsFeed22He8bztA4Q60GwS0vwEIGQAE9TXdx78bMPpboNtdQMj11ceIcL4eERERkcOFh4dj1KhRAIDFixe75JpxcXGIiYlxybWIyPUsBXs5JWUuXIn0jD1LRDOjzuYXo8pgaHatOAEgTOWNZwZ1w8Kbo3BH99Bm0Q55SIc2eDmmJ+YO7YFO/qatMMNU0jPvFHLnfG62tONk5RK1VFKtOBn4EZlisEetVrsAFboIKvPW7TwEn/Gvou2U+Yh+9H3sPpLp+sW1NoYq4PAKYMOdwOZJwM5/ASU5wLmd9p3Hp05IJ5MBg/8J9Lof6HAj0PtBIPZNwK3WD+ZyiR+GfTuItxMRERFRgzz++OMwGAxYsWIF8vPzXXJNrVb8UB8RNX+WWnHmllRX0JVVVuFYTj4uFpY4bR0lFZUoq6wy265ytxzAhAkqwkorq3CxsBQlFebn81GyUsuVQhthpleEWjpMrKFhsEctlFIuRyeNebg9q3+nRlgNUdPFYI9atfFDe1t8/djZy3jgnW9Zueds2QeAI6sAXH3TcnEfsOvfQPFl28/h4Q8oBT9wK72A7vcAA+YA3e4EFHV++K2QaG/h6W/7tYmIiIjIZpGRkcagbeXKlS65plqtdsl1iMi1Sioqcb5AOqzLLS7DidwCPLxpPx7dehD3/rgPyw+ccspaRPP1AEBlpWJPqlXo8Rzxgw9NvWKvpWnnKx7R0SfYef+uRGisB3us2KOW7IFoLRS1qn27B/jiRm1QI66IqOlhsEet2uNjb4CHlcHTx89dwY60DBetqJU6m2i+Le+4fefwaVu/a4cMEG9Xd67f+YiIiIjIqilTpsBgMOCDDz5wyfX8/fnQFlFLlKkvgsHC67kl5fjwr5M4dzX8MwD45vBZpGYLZrk3kL7MfL4eYD2AkaoIS72kF25nsOda7go5BrQz/zdkTFeJsR4OYEsrTo0ngz1quW7o0AbLRvXDI/064oUbuuPtW6PhyVacRCYY7FGr1i5AhQdv7291v+2H0p2/mNYs4+eGn6NuG05bdYgFUKcvvmcbIDCqwUsiIiIiIrGpU6cCAPR6PVavXu3068XGxuKll15y+nWIyLXS86Tn6wHAkSv5OCQIyBKy7OgOY6OLEpWDaivBnlTF3rqj54Tbfd3ZitPV/q9PBAK93I0fD24fgKFh5qNdHCXUzwtuFub3yWWAj5WH1ImaO63aB3f17IBbOgbDg6EekRkGe9TqPTchFkqF5b8KCQz2mj7f9vU7zjsE6DkFxm+HSi8gerp5y04iIiIichiVSoWYmBgYDAb8+eefTr9eZGQkZs6c6fTrEJFrZegsB3unJObvSYVmDXE8p0C4vYOVGW0+bkoE2FF91TdEY8+yyAG6Bvjik9H98fqw3vjvrdH4z7BeUMqdd0tVIZdZ/HP2c1dCYSH4IyKilo+Pd1Crpw32x5Sb+uKLX/+W3Gf3kSyUV1TCjU+INF31rdgDgB6TgdDY6pl+Ki3gGeC4dRERERGR0PLly6FSqRp7GUTUjJ2WCO4awzFBsOelVEhW5NUWqvJCTom4lWdtN2uD0FZi5hs5l7ebEoNCXXev4LEBnfHUL8m4Ulxm9lobLw+XrYOIiJomVuwRAXht6q1o6+8r+XphSRkOnHT8E30EoMr6mxebNCTYAwC/MCC4H0M9IiIiIhdhqEdEDZVupWLPEp0NQZqtDAYDjueaB3td/H0gl1mvrOrg5211Hy+lAg/361iv9VHz087XCx8O74uuAeb3qmKc2AaUiIiaB1bsEQEIDVRj+5vTsWTzHqzbmYZTF3LM9tl+KAMDu4c1wupauFIHDS33qWcrTiIiIiKiZmzatGlwd3c32/7II49g+vTpjbAiItcor6zC5aLSeh+fritEH0+NQ9ZyubgMeYKgUBTKiFhr1wkA90aFI9CblVqtSaC3B965NRqfHEzHlpMXUF5VhVs7huDunh0ae2lERGSnZcuWYfny5Wbby8rMK7NtwWCP6KqObQPwxoMj8cqkmxFwz+uorKoyeT3h0Gk8PT6mkVbXgpWYh6h2C+gJePCJbyIiIiJqfXJyxD9PFxSI530RNUep2Tp8f/QcsvTVFXo1LS4NDThnhq4IfRw0r05qvl7XAD+bjrfWrrOdryfGdefDrK2Rp1KBR6/vjGl9I+Aml3O2HhFRM1VQUIALFy447HwM9ojq8PXyQP8u7bH32BmT7Rv2HMGQZ5Zizp1xuOOGXo20uhbIEcFe1zsbfg4iIiIiomYoICBAWLHn62tbpRBRU3c8pwBzfk9BeZVpjHf4Sn6DztuQNp51HcsRr8VRFXvxXdpCKec0ndbMU6lo7CUQEVED+Pr6om3btmbby8rKJB/Us4TBHpFAbGSEWbAHAHuOZmHCvJX4cOYYzIwf3Agra4FKcht2vEoLtBvkmLUQERERETUzn3zyCQYOHNjYyyBymp9PXTQL9RwhPa/QYecSVex5KuU2tdgEqivypCjlMtzeKaTeayMiIqLGN336dGGb/L1792LcuHF2n6/FPO6zatWqxl4CtSAxvSMsvj57yQZ89st+1yympbOlYs+zDaDpIn6t2z2ArMV8KyMiIiIiIqJazuQXO+W86boiGAwNDwwNBoMw2Ovi72tz20Q3hfR72iEd2sDf07wql4iIiFqvFnE3PDMzE88//3xjL4NakNjeEfBws1zQ+vD76/DT/uMuWlELZkuwFxgJBAjan/q0BzrEOn5NRERERERE1CToSsqdct78sgrkOODcV4rLkCs4j61tOGsMaOcv3B7fxbxtFxEREbVuLSLYy8jIaOwlUAvj7+uFp+4YanEfg8GAJ5dvRGVllYtW1YxUlgP5WUDxZev7ltrQitO/G9B5DOBW642RTAH0e6z6v0RERERE1GTllZTheE4BjlzOR+olHZIu5mH/+VyU8b1Uq1JcUYkz+mIUV1TadVxeaZmTVuSYdpzHBNV6gP3BXmSwymxbe19P9A3R1GdZRERE1II1yRl7WVlZSElJQVJSEnQ6ncV98/LykJiY6KKVUWvy+n23IbpjW/x28CQ++fkv4T7Hzl7GtztScU9ctItX14TpM4E984CCrOoWmV3GA5EPSu9vS8VecH/ANxS46QMg42egohgIuwnw7+q4dRMRERERkVP8nn4JS/4+Zbb9q7ED0NbCbDFqOXaeuYJ39hyDrrQCag8lnh7UDUM6tLF6nMFgQJ6TKvaA6nac/SUq5Wx1WiIc7GZnsDeqc1tsPnEBFwtLAQAyAE8N6gq5zLZ2nkRERNR6NKlgLz8/H8899xw2bdpk13EGgwEy/qBDDiaTyXBPXDTuiYvG8sfH4d8rf8N/Vv9utt/Cr//E3bFR/BqskbK8OtQDAEMVcPxbICgaCLlevH+JlYq94OsAVXj1731CgF73Om6tRERERNSkLFiwAHq9HgsWLHDoeV944QXIZDKHn5dso5SYM1ZR1fD5ZtT05RaXYdGuoygqr67U05VW4K3dx/D5mOuh8nAz2fdiYQmy9MWIUHsj0NsDReWVKHfi18kpB1TsZV8N4mqTy4AOft52nUfj6Y4lI/vh51PZyC8tx00RQdCqfRq8PiIiImp5mlSwN2LECGRmZjpkeDGRo7088Sas2paEE+evmGxPTr8A5ZiXMW5Ib8y//3Z0Cw1spBU2AeVFQPbf5ttPbxYHewaDdLDnHQy06Q1ETnPsGomIiIioydq4cSMyMzORmZmJlStXOuSckydPRmJiIsLDwx1yPrKfVLBXyff+rcLWUxeNoV6N/LIK7DxzBSM6X5sftzYtC58mpaPKAHgo5HhmUFd0DfBzyBrc5DJ4KhXIL6sw2b7n7BWUVVbBXVH/STUXC0vMtgV5e0Ah8XVviZ+7Gyb0CK33WoiIiKh1aDLB3pIlS5CRkWGsetJqtYiJiUF0dDQ0Gk3jLo4IgEIhx5y74vDI++uEr6/beQgHTp7DX+89Cn9fLxevronIzxJvP78bqCgBlHXa7JTlA4YK8/27T2JlHhEREVErtGrVKowcORIJCQmYMmVKg8O9yZMnIyEhAWq1GqtXr3bQKsleCrk4NKlkxV6LV2Uw4PsjZ4Wv7TqbYwz2TuQW4JOD6aj5iiitrMKH+0/ipSE9HLKOuPBAKORy/Hzqosl2XWkFlh84hQi1D7oG+KJ7G/uDxOwi84q9YB+Peq+ViIiIyJomE+wlJCQYf//oo4/ixRdfbMTVEInde1NfvLb6d2RdEs9+TL+Yiw/W78Qrk29x8cqaiPxM6dey/wbaDQIyfgX0GUBAj+q5eSKeDZtxQERERETNk1arxZYtWxwS7tUO9bZs2cKKvUaklBhbUGGocvFKyNUOXMhDXql4Rt7fF3KN1XLfHT6LujGvvrQCu8/aMJO9lrY+HrggaI05qks7ADAL9gDgx2PnAVTPtHu4X0fc1bODzdczGAzCVpzB3pwdSURERM5T/14DDpaZmQmZTIbY2FiGetRkubsp8dz4WIv7vL9+F/IFT+y1ClIVewCQ+Tuw/SXgwHvAyR+AfQuBvRIzTjwDnLI8IiIiImr6asK9sLAwJCQkID4+3u5zMNRrWqRaEnLGXsu36cR5yddKKqqQdFGHovIK/JqeLdzn4MU8u643pmt7s2092vghKkiFqCAVwlTS3XUMAL5MzkB+mTiIFNGXVqC00jygDmHFHhERETlRkwn2MjIyAABTp05t5JUQWfbwiAG4pW9nyddzC4qxdPMeF66oCbEU7J3fCVxOMd1WeE68rwcr9oiIiIhas9rhXnJysl3hHkO9pkcq2GMrzpYtp7gMO89YrrjbffYK/si4JPl6uq7IrmsObO+PWf07QXG1SrSTxgcvDe0OmUwGmUyGUbVm+omUVFbhyOUCm693sch8vh7AYI+IiIicq8kEezVvtvimi5o6dzclvp87BZ8+NQGR2hDhPu/8sB1FJWUuXlkTYCnYswcr9oiIiIhavZpgzp5wb9SoUUhMTGSo18RItuJksNeiJWZdRqXB8p/xnnM52HTigsOuGeTtgXHdQ7H+7iFY8Y8BWDqyH9r5XqvSu71TCNwkguYaZ/JtDxMvCtpwVq+DrTiJiIjIeZpMsFdTqZeSkmJlT6LG5+vlgftvuQ5JHz2OYZEdzV7PzivEPYvWoKy8ohFW10gqy4BCB70h44w9IiIiIoI43MvPzxfuO2rUKCQnJ0OlUjHUa2KUUhV7VkIfat4OXxb/Xa3tYmEpjufYXiFniZdSAW83BQDAXSFHiI8nZHVCZZWHG2LDAi2e54y+2OZrXpII9lixR0RERM7UZIK9WbNmoXfv3vjqq6/sPlav1yMsLMwJqyKy7qWJNwq3b953FF7j/oU297yG+97+Bjl2PPXXLBWcBWA+W8Bubr6Awr3h5yEiIiKiFqFuuDdx4kSzcK8m1NNqtdi1axdDvSaGrThbp+M51oM9Rwr0djcL8kQm9g6zWLV3Jt/2YO9iobgVZxCDPSIiInKiJhPsAcDatWuh0+mwevVqu47Ly8uDgU/6USO5pU9n3NBD+sZBXmEJVv5xELe+9EnLDvcc1oaT1XpEREREZKp2uJeUlISJEyeioKC6yqd2qLdlyxaoVKpGXi3VpZSLbz1UVDngwUBqkorLK5FlR+WbIwR62RamddT44N9xvdGzjZ/w9bN2BHvZReYVe2oPJbyUCpvPQURERGSvJhPs5efnQy6XY+HChZgzZw4WLFiArKws5OfnS/7KyspCVlYWPvroI5ueyiJyBplMhv89MR4Bfl4W90s6fQGjXvkceonh2s2eo4I9DwZ7RERERGROrVZj69atiIyMRFJSEoYPH85Qr5lQSM3Y4wO6LdaJ3AK4+k/XTWH7faEB7f3x/vC+iO/S1uy17MJSlFZU2nQe0Yy9YM7XIyIiIidTNvYCarz++utYtWoVAMBgMGDx4sVYvHhxI6+KyDY9woKw9bX/w60vfQK94Im9GvuOn8WYV7/E5v88AB/PFtZuUhTsyd2Amz8ESnKrP3bzATzUwM8PAVVl4vP4d3feGomIiIioWVOpVFi7di0mTpxonM/OUK/pk5yxx1acLZbU3LxXYnrirT3HUFRuW3Bmj4p6fD11EDygawBwrqAEHTU+Vo/PFrTiDGYbTiIiInKyJlOxFx8fD4PBAIPBYKy+q/nYll/OoNPpMG/ePPTq1Qs6nc4p16CWo3+XUGz69/3Q+Fh+Om97WgbGvbYCJWXldl/j7GUdfjlwAgXF0uGhs5WVV+D3pJM4cPKc6d89UbDnF1b9Kyi6+pemM+AVCPSdLT65VyDQcZRzFk5ERERELYJcLoderzfp2iKXaPVITYPUjL36BDHUPBwTBHsyANe388cD0doGnbuTRODWLcDX7nOFqbyF28/Y0Ea0pKISutIKs+0hDPaIiIjIyZpMxV5cXBzUajX0ej0MBgPUajUAQKPRSB6Tl5cHAA4P3TIyMvDRRx9h5cqVJteqWVNDJCQkYOPGjUhOTkZmZiZ0Oh3UajXCw8MxduxYTJkypUHXcfb5ybIhPbVI+uhxrPkzGbuOZOKHXWnC/X5LOomJi9bg+7lTbL4J8drq3/Hqyt8AAF4ebtj87/sRF9nRYWu3xbGzlzH85c+QeSkPADBuSG+sef4eKOUACs6YH+AXJj6R9lbArwNw8S+g7OobPt/2QGgM4BnglLUTERERUfOn1+sxcuRIZGZmIjw8HCqVCikpKRgxYgS2bt0KX1/7b+yT8yklWnFWshVni3U8J99sW7jaG15uCozt2h5bT17EqbxCk9ejg9VQeSixPeuKxXPfHBGEC6klKKrTLvPWjsF2rzNUYqRGVn6R1WOzBW04ASDYysO+RERERA3VZII9AIiJicH27duxa9cuu9qobNy4ETNnzmzw9VNSUvDBBx9g06ZNDT6X6NzTp09HRkYGACAqKgrR0dHIy8tDSkqK8de8efOwaNEiTJ06tUmdn2zXIVCNZyfEAgBOXcjBjc9/jLNX9Gb7bdhzBCv+SMJ9t/Szes5fDpwwhnoAUFxajgnzVuLE/56F2oVvGmYvWW8M9QBg3c5DWP1nMu4NzwCqBBWIUsEeAAT0qP5FRERERGSDmlAvIyMDUVFR+Prrr1FVVYV77rkHqampDPeaMKlWnKzYa5mKyyuRJah46+pf/XdTIZfhhSHd8dxvycaKt2BvDzw7uBt+PnXRarDX3tcLUyLD8fHB08ZtwzuFQKu23jqzrra+HlDIZGYhsy0Ve9kSYzjYipOIiIicrUkFe3379kV+fr7dsxGio6PrfU2dToeVK1dixYoVxlDM0VasWIHnn38eADBlyhTMnTvXpGpOp9PhueeeMwaKzz//PDIyMjB37twmcX6qv05tA/DLvAdx4wsfI7vO04gAsPCbbZhyYx8oFNVVe1mX8vDEso1ISE1Hh0AV3npoFG7t2wWvr/7d7Nic/GIs2bQHL9w9zOmfR83afjt40mz7j7/9gXuv/1V8kKVgj4iIiIjIRnVDvbVr18LPzw8AsHXrVowYMYLhXhOmkOhSUllV5eKVkCuczC2AKLLtWqtVZkeND5aMvA67z16BQibHDaEB8PdyR4RG3BqzNo2nG2LDAxEZrMLxnAK09fHEwPb+9VqrUi5HO19PnMk3DfLO5tsQ7Anm6wHVISURERGRMzWpQQSxsbGIjIy0+ziNRmP3cTqdDhMnTkSvXr0wb948Y6g3ZcoU7Ny502HtKhMSEoyh26xZs/DGG2+YnVutVmP58uWIj483blu8eDE2btzY6OenhuveIQg/v/4gAgQtPo6euYx1V9t1ZmbnIW7Ox/hx92HkFhQjJf0i7py3Cqv/TML2NHHo/O6PO1BUUubU9df4cfdh4fZ1SRJPU8rdgMAoJ66IiIiIiFqD2qFedHQ0tmzZYvYw6NatWxEZGYn09HSMGDECBQXm872o8SgkWnGyYq9lOnLFvA0nYD4DL8jbA2O6tseoLm3h7+UOAIhQ2xbsAUCvQBX+0a09BoUGmMzctFcHlfl79bpBn8hFiVacnLFHREREztakgr3IyEi89NJLdh+nUqmg1do3fFmtViMxMdH4+7lz5yItLQ1vvPEGtFqtxdl+9pgxY4bJNSx58803TT6eM2dOo5+fHCMqoi3++8ho4WtPLd+I0a9+gdg5y03aXAJAfnEppr75teR5L+kK4Xfnv/HEso04dSFHuM/3Ow/hwXe/w2urf8eZyzpkZOfi1ZW/4qH3vseGPYdhMBiw8o+DeOCdb7Hw6z9xSWdeWQhUt92UkluqMN/Y51HAQyN5DBERERGRNfn5+Sah3ubNmyX33bp1K2JiYhjuNUGSrTg5Y6/FScy8jM+TzR9MlQHo7G+9kraDnzf8rwZ3UjRWXrdXB8FDuPrSCuhLBeMmahHN2PNQyKH2cOz6iIiIiOpqUq046yszM7Nec/Hi4+MRFxcnnDdnbztQkcWLF0On0wEAZs+ebXV/tVqNWbNmYfHixQCqqwpXrFghOQ/P2ecnx5oYF4V/r/zNLIA7l5OPc4LB4vb4cMMufPHr39j62gMY3CPcZPsTy65VZr6zbjsMhurAEAA++2U/OgSqceayzrjPtztSsG3hw/D1uvaU4SVdIRIOpUte/+AVL9zUvtaNk6hHgIjbG/Q5EREREVHrlpmZiZEjR0Kn0yEuLg6rVq2yesyaNWswceJEbN++HYMHD8bWrVvRoUMHF6yWLJEK9ipZsddiGAwGrDqUJQz1ACBM5QUvN8EDoXUo5DLc3ikEa9POiF+XyeDr5thbWaKKPaB6zl6vIOmQ7pJgxl6Qt0eDqgeJiIiIbNGkKvbqq76z8ZYvX+7UUOvDDz80/r52G0xLxo4da/KxpXaZzj4/OZZSocBzd8Y67fz5xaUY+uwyzF6yHl8npuDAyXOY8+lWk330RaXGUK9G7VAPAA6cPI/3ftxpsm39nsOosvCm+9NjbbAhU4ULRUogMBrockfDPhkiIiIiatXqE+rVWLNmDWJiYpCXl4cRI0bgzBlxQECuo5Cq2GOw1yKUVVZh0a6jkqEeAMSEBdp8vpGd20q+pvZ0c3hwJqrYA4AsK+04RcFeMNtwEhERkQu4rGIvNTUV06dPh06nw7JlyzB06FCT15csWWJsjWmPvLw8pKSkOGqZDrNx40ZjNZ1arba5VWhUlOlMMqn/J84+PznH/bdch9dX/4GzV/ROu8aSTXuwZNOeBp3jvfU78cQ/hhir9r7/LcHi/qtOBmDVyQCo3Crx8UQt7mzQ1YmIiIioNWtIqFdjzZo1eOSRR7B582aMGDGClXuNTCkRxFSyFWeL8M6eY/gt/ZLk69e11WBKZLjk63WF+nmhX4gGBy7mmb2WU+z4GfMdVOK5fqdyCwCECF8zGAy4LFhLm6uzAomIiIicyWXB3vTp05GZmQmDwYDp06cjNTXV5HWVSoWEhIR6PXllMBiaXKuDhIRrQUh0dLRdx0ZFRZmElQkJCYiLi3Pp+ck5PNyUePvhUZi4cE1jL8WiK/oifLx1H54aF4Nlm3Zg66ErNh2nL1dg8sqzUIQdwrghva3uX1FZCaXCejsWIiIiImodaod6o0ePxtKlS+t9ruXLlzeZcE+n00GtVjfKtZsCpVzcLIitOJu/y0WlFkO9MV3bYVb/TpJfA1Liu7QVBnu9Ahs+NqWuAE83aDzdkFdiOlPveI70nM78sgqUVVaZbQ/yZsUeEREROZ/LWnH6+fkZAzjRG5oxY8YYf28wGOz61RTVbnEZHm77k2mAeVCXmZnp8vOT89wVE4XVz0/E8P5d0S000ORXdIR0yxEA8PZww6lPn8WcO+PQt1M7p67z7XXbsXzrXjy6dLNdx1VWGTDpjbX4af9xyX2u6Isw+tUv4Dfh34ic+S427j3S0OUSERERUQswadIkh4R6NZYvX45Ro0YhLy8P99xzjwNWaJuUlBTMmTMHvXr1QmhoqMl/R4wYgTlz5tR7pERzJNGJExVV5sEINS9ZenG7SrkMmH19Zzw+oIvdoR4ADOnQBhoP8/l2Xfx97D6XNTKZDF39fc22n8gtRJXEPSdRG04ACGSwR0RERC7gsoq9t956C88++yzy8/OxbNkys9dVKhWioqKQmpqKuXPnIjw8HCqV9Sex9Ho9vvrqK+zYscMZy64XnU5nbJMJwO4nMyMiIkw+rvuGz9nnJ+e7OzYKd8dGCV9bsmk3Zi/ZIHztkREDoQ32x4IHhmPBA8NRWl6B8a+vxNb9xxy+xvM5+Zj54Y/1Ora8ohJT3lyLI8ueQqDa9I2XwWDAhHkrkXgoHQBwOOsS7lm4GrvfmYkoK8EmEREREbVso0aNgl6vx6JFixx2zuXLl2PevHku6fKi0+nw3HPPYdOmTZKvp6SkICUlBStXrsSUKVMwd+7cFl/NJ5PJoJDJzFpvcsZe85dXIm6N+ezgbrito7iNpS3cFHLcH63Fe/tOGLcpZDLc3qn+57SkW4Av9p3PNdlWXFGJM/pihKvNW3VeLhJ/3kHebMVJREREzmdzsDd58mS89NJLiIyMrNeFIiMjsXXrVov7REdHQyaTYebMmXadOzw8HKNGjarXupyhbgWcrfPvatStwKs7Q9DZ56fG9Y/BvYTBnrtSgWfGx5hs83BT4tuXJmPW4h/x5W8HXLVEm+QWFOP99Tvxn3tvM9m++a+jxlCvRklZBf6z6nd889JkF66QiIiIiJqauXPnNqvz1paRkYFJkybZ9eDkypUrsX37dmzZsqXFh3tKuQyVlaZBHmfsNX+60nLh9p5t/Bp87vgubSGTAT+fyoafuxJ39QxFdwecV6RrgHnFHlDdjlMU7LFij4iIiBqTzf0QEhISkJyc7My1IDo6ul5vZrRabZNqyZmUlGTysUajset4a5WKzj4/Na72bVS4Maqj2fYHbr0O7duY/9l5ebjhs6fuxJU1LyPpw8eFxzrS89EXbN73w427oSssMX5sMBiwYO2fwn2/33kIh7OyG7w+IiIiIqLG8Pzzz5uEelOmTMHq1auRlpaGtLQ0rF69GrNmzTI7LiMjA9OnT3flUhuFQtCPkxV7zV/duXQ1NJ4Nr1yTyWSI79IO793eB6/f2Bt9QjQNPqeUrgHiwFBqzt7lYnGwF+TFYI+IiIicz65G53q93lnrAFD9xqc+LVdUKhVWr17thBXVT0P/P/n7+5t8nJeX59LzU+N766FRUPt4Gj/u2r4N5j8w3OIxGl8vREaE4Jd5D+LR0YNNXps4LBqhtULBjiH+mHpTX5N97r25HzzcLBfx/rPfecwfcB4d/cRvYurSFZag14z/4tvtqcgvKsW2lNPYdUR6puPCr01Dv6NnLuHjrfvwwfqd+GD9TmzYcxjlFZU2XZuIiIiIyJUSExMBALGxsUhLS8Mbb7yBuLg4qNVqqNVqxMXFYe7cudi6davZA62JiYktvpOKUhDsVTLYa/byBBV7SrkMPm6KRlhN/QV5uwtn+h3LzRfuL2rF6SaXQeXhsok3RERE1IrZ/BOHWq3GV199halTp8LXV9yiwBHqtom0VWxsrINXUn+5ubnWd7KgbkVd3SDP2ee3VVpaml37h4aGIjQ0tF7Xam36dW6Pw0ufwoa9h+Hl7obxQ3rDS/AmQ0Qul+P9GWMwcVg0kk6dR99O7TG4RxhyC4qxdf9xKBVy3N6vC1TeHpg1ejD2Hz+Lob206NOpHe6OjcL4eSuF4dkzURfxr37V1XqDgwtxOt+2JxEv5BbgnoWroQ3WoLS8wuK+q/9MxtyJN6FbaCDe/3Ennv1kCyqrqkz26d+lPTa+ej+CNc77PkRERETU3Jw9exZnz561eX97f5Yny2pCuaioKKxZs8bivlFRUVi6dCkmTZpksj0xMRFRUeI53C2BUjDjsKIJdd6h+hFV7Kk93Fwy09KRZDIZugrm7J3MKUSVwQB5nc9H1Ioz0Nuj2X3eRERE1DzZHOxpNBpkZmaiZ8+eiI2NRVRUFGJjY9GnTx/4+Tmnx3lzpdPpTD52dOtLZ5/fVvbOqXj66afxzDPPOGk1LU+Ivy8eGj6g3scP6anFkJ7X5i8G+Hlj8o19TPYZ1D0Mg7qHGT8eNaA7Vs25BxMXrjEJ1Gb1vIRFA86h5j3KY6P6Y+2Hp1BluPamZdVDffDJvgL8lnRSuJ6M7Dyra66sqsKgpxZjzKCeWPnHQeE++0+cw+0vf4qvX5wM76thp5tCgWCND99EERERUau1Zs0avPPOO429jFarplrvzTfftGn/uLg4xMfHY9OmTcZt6enpzlhakyFqxVn3Ib7WrrC8Akev5KONlzu0ap/GXo5NcgXBnsbTtodSmxpRsFdUUYmz+cUIU5nO2bssDPYa3n6UiIiIyBZ29wgwGAxISEhAYmIiFi9ebNweFRWF6OhoREREIDIykoFfLQ1tnWktuHP2+al1GT+kN/a+OxMLv0lA4cVjuDf0CO7qmIfamdmgm+7BH7578N66P1FUDjx4a19MGDMBwZ1OSQZ7ttIXlUqGejVS0i+i5/T/mmwLC1Jj2exxGN6/a4OuT0RERERkr4MHD0KtVttVcRcXF2cS7NVn3nxzopCZTwLhjL1rki7m4bXth6Erre5ycke39nj0+s6NvCrr8krMW1KKWlo2B10DxF1hjucUCII988+b8/WIiIjIVWwO9mrmsNW0vMzMzDQZDJ6cnCycCcDAz34ajcbix039/NT89e3UHmuenwhsexLIzTN90TsEcPdDzNBbETP0VpOXbozqiFv6dsZvBxsW7tVH1iUdxv7nS3w3dwpGD+zh8usTERERUeuVmZmJKVOm2HVM3TEUWq1WYs+WQTRjj8FetSqDAR/8ddIY6gHAD8fO4ZaIYPQIbNr3T0Qz9jSezbNyrZtEsHfsSgFujgg2flxYXoEiwfiKQG8Ge0REROQadlXsTZkyBQsXLjTZlpmZiZSUFGRmZuLgwYPGj2skJycjOTnZrEVeVFQUwsPD0bdvX0RGRkKr1SIsLAx0LURt6uefN28eevXqZfP+nK/XzFRVALrT5tvV0k+NymQyrHl+IiYuWoM/kk+hysIbdXelAqvm3IN5a//AgZPnHbFiVFRW4a75q7D+X/fhtn5dHHJOIiIiouZg4sSJds0dT0tLs7u1PknbunWr3cdkZmaafDxmzBhHLadJEgV7lZyxBwA4nVeIDF2R2fbdZ6806WCvrLIKReXmAVdzbcUZ5O0BtYfSJGAFgLTLpl2SRG04q49vnoEmERERNT82B3s6nQ7R0dFm28PDw82eNASq20MmJSUhMzMTycnJxv/WzIerCfxqtx4BWkbgV7eFSkODtLqtMp19flv16tULAwcObNC1qQnLzwKqzJ++hMZyO5gAP2/8/PqDyCsoRuYlHUa/+gXOXjF9I6RUyPH1i5MwZlBPxPaOwM0v/g+HMrMlz3nHDb3w89/HUSR4GrSusopKjHt9BTb/+37ERXa0uj8RERFRSxAaGsoH6ZqZ5ORk4+9nzZrV8ltxsmJPUuol8XiNDSfO44E+Ea5djB10Eu/PmmuwJ5PJ0LONCrvP5ZhsP5ZTgJKKSngqFQDEbTgBVuwRERGR69g9Y89WKpXK+MRo7ZYker0emZmZSEpKQkZGhrHar6atp6XAT6vVYsmSJc5assP4+/ubfGzvDLy6+/ft29el56dWrFQPXDoAyOSAPlO8j5Vgz7ibrxc0vl5I/uhxTJi3EttSqqv/VN4e+OTJCRgzqCcAIFDtg5/nPYj4f32Bg6dMK/fkchkWPjAcz4yPRfrFXIx85XMcO3vZ6rWLS8sx5t9f4ufXH8Sg7s3rwQAiIiIiavl0Oh02btwIoPq9bmuonlTKWLEnJTVbJ9yubuKz6vJKJIK9Jr5uSyKDzYO9SoMBR67ko2+IBgBwSaJiL5Az9oiIiMhFbA72Fi1aZFdrFykqlQqRkZGIjIw0e62mjWdSUhJWrlxprO4Drs3waw7BXt0KuPT0dLuOz83NNfm4bkWks89PrZTuNLDzFaDkiuX9NF3tOq3G1wu/zp+Gn/8+gZyCItwc3Rkh/qazC9r6+2Hn2zOwed9RnL5Q/fXp5emGuN4R6K0NAQBEhPhj/3uPYvNfx5CZnWc8dt/xM/g60Xy+Z0FxGUa+8jn2/ncWurRvY9eaiYiIiKjh8vPzm/V8dWeu/7nnnoNOp4NWq8XatWudco2mRlSxV8mKPRgMBqRIVOzll1UItzcVeSXiyrXmWrEHAFFB4srZlGydMdiTrthjK04iIiJyDZuDPXsHgddHeHg49Ho9PvzwQ+j1epO5fIZm9CRfnz59TD62t1Vm3Yq6ukPUnX1+aqWOf2s91AvuB3j6W95HQCaTYXh/y4Ggh5sS44b0triPt6c77owxfSigqqoKXh5u+OLXv8321xWW4KmPN2HDv+4zey23oBgeSgW8bRjsXlhShsqqKqi8Pa3uS0RERETVRowYgdmzZ2PSpEmNvRS7rVy5EvPnz8ehQ4ccfu45c+Zg06ZNiIqKwtq1a1t8C84a4lacVY2wkqblQmEJrhSLg6K8knIUl1fCy03h4lXZRrpir/kGXF0DfOGhkKO00vRrMyX72n2Uy8XmFXtyGeBvw3tLIiIiIkdwWitOe+Xn5+O5554za8FZE+jFx8fjzTffbIyl2a1uBVxqaqpdx9cdoh4VFeXS81MrdSnZ+j7dJzp/HXaSy+X4+LFxKC4tF1bubd53FAdPnUPfTu0BACVl5bjv7W/x/c7qmzQzRw3Eu4+MhkIhNzvWYDBgzqdb8f76naiorML4Ib3x6ZMT4MfZCURERERWLV26FCNHjkR6ejpefPHFxl6OzebPn48lS5Zg1apVDTpPTQcatVoNnU6HxMREzJ8/HxkZGcZKPUeEemlpaXbt31jzGJUy85+3OWPPNDASuVBYgo4aHxetxj55LWzGHgC4KeToGeiHgxdN26MevqxHRVUVlHK5sBVnGy8PYXhNRERErcvZs2dx9uxZm/e392f5Gk0i2FuyZAnmz58PoPpGek2lnsFgQFRUFN58801h686mSq1WG9+8ATDOD7RV7daaovanzj4/tUIGA1AqnutgFNALaNM0/x4qFHJ8+cxdKCmvwPrdh81eX/D1n1j7QvWT4nM+3YrvdlwLwxdv2oPe2hDMGDXI7LgVfxzEO+u2Gz/+fuchhAWp8c7D8U74LIiIiIhalqioKCxduhQzZsxAamoqli5d2uRbc86YMQObNm3CwoULG/xeSfTgao2MjAz06tULUVFRGDt2LGbNmlXv69g7n+/pp5/GM888U+/r1ZdS1IqzGXXmcZaUS5bfh50vaMLBnkTFnroZB3sAEBmkNgv2SiqrcCKnED0C/YStOIPYhpOIiIgArFmzBu+8847Tr2P+yJwLbd68Gb1798b8+fONlXkymQwGgwHh4eFYtmwZtmzZ0qxCvRoxMTEmH6ekmFcSSUlOvlY5FRcX1yjnp1amohgwWJnf0P0eQDDwvqlwUyrwv8fHw0fQ/uTb7anwm/Aqxr2+Ah9t3G32+vIte4XnXLJpj9m2T3/ej9Lypj3rgoiIiKipGD16NFatWoU///wTN9xwA7Zs2dLYSxLavn07evfujU2bNmHp0qUOGUWxfXv1A2JarRaxsbGIj483G4OQkpKCefPmoVevXkhISGjwNZsycStOBnvWKvbOF5S4aCX2E1XseSrl8FI2zdahtooKVgm314Swooq9QHZ1ISIiIhdqlGAvNTUVo0aNwvTp06HT6YxVegaDAQaDAXPnzsWOHTsQH998q2LGjh1r8nFiYqLNx9YO6aTeUDr7/NTKWKvW03QBQq53zVoaoI3KGzNGDRS+VlRaLqzmA4Ck0xdw8rzpfMEzl3XYczTLbN/84lL8evBEwxdLRERE1ErExcVhy5YtqKqqwiOPPIIpU6Y4ZXZdfWRlZWHGjBnGOYCrVq1y2PvQ2bNnIy0tDTt37sSaNWuwfPly7Ny5E2fPnjWr0NPpdJg0aVKLDvcUgocEK1t5sHepqBRn84st7nO+wPLrjSmvxLxyTePRvKv1AKBnoEr49ZqSrYO+tBz5ZeYPegYz2CMiIiIXcmkrzrpz9OoGelOmTMHcuXOhUomfjnIlvd7yU3PWjB492uTjFStW2NReZePGjcbfx8fHS85ccPb5qZUpsxDsuauA659r0tV6tT09LgYfbthtd1XdD7vS8Mz4WBw8dQ5/JJ3C1r+PS+67bmca4gf0aOhSiYiIiFqNqKgo7N69G4888gj+/PNPJCQkIC4uDrNmzcLQoUNdvp7t27dj8eLFSExMNI6AWLt2rUPfi1p6fzZ37lzExsYaA8UaM2bMsHvORk3Fn60aY74eIG7FWdHKW3F+kWx9rEaTrtgTtOLUCDqoNDdeSgW6BvjiyJV8k+1/X8gz21ajg8rLFUsjIiKiJm7ixIl2tfRPS0uzu7U+4MJgb8GCBVi8eDGAa4FeTagXGxuLRYsWITw83FXLsSovL8/k4/oEfXPnzsW8efMAVM9QqHnzasmHH35ocnxjnp9aEamKvYiRQNRDgLL5vElp6++Habf3x2JBG01L1u1Mg7enO2YvXm913/V7DqOishJKRfNuMUNERETkSiqVCmvWrMHGjRsxZ84cY8CnUqkwdepUjBkzxqljGFJTU7FhwwasWLECer3eOA5i0aJFjdLJJC4uDvHx8SZz+HQ6HTZu3Gj2IKclvXr1wsCB4q4VTYlwxl5VVSOspGlIu6THT6cuWt2v2QV7LaBiDwD6hKjNQrzSyirJMLaDX/N5z0xERETOExoa6pIH6ZzeirNmjt7ixYuFc/RWr16N1atXN6lQLyEhATqdadDx1Vdf2X2eWbNmmcxQeOGFF8zOW9uKFSuMbTIXLVpkNn/B1eenVkSqYq/jyGYV6tX456Sb0THE365jdh3JtCnUA4Ar+iIkHrL+dC0RERERmRs9ejR2796NmTNnwmAwQKfTYfHixRg5ciTCwsIwefJkLF26FJs3b0ZWlnlrdFtkZWVh8+bNWLBgASZPnoywsDCMHDkSixcvNo6DmDJlCtLS0hp1PMGbb75ptq2ltuNUyM1vP7TWGXuVVQZ88Jdt7f0vFJSgqglWNhoMBuGMPY1nywj2busYLNx+LKdAuL2DytuZyyEiIiIy4bSKvdTUVMyZMwcpKSlmgZ5arcbs2bMxc+ZMZ13eZikpKUhMTERubi50Oh0yMzOF8+pWrlyJ7du3IzIyElqtFv7+/oiMjLRaIbdlyxbccMMN0Ol0yMjIwMiRI7Fs2TJERUWZ7Ld48WJj9d2sWbMwdepUm9bv7PNTK1EqUZHq3vhtcesjWOOL3xZMwyc//YWt+49h/4lzDr/Gup2HcFN0J4efl4iIiKg1UKlUmDt3Lh577DGsX78eK1asQGpqKoDq+eGi92QqlQoajQZqtRoajQYqlQp6vR55eXnQ6XTIy8uT7LRS855Uq9Vi6tSpmDJlSpMYAaFWq6FWq00e0KzbPaalUHLGntHOM1dwIrfQpn3LqwzIKS5DYBOb4VZcUYmySvOKy5YS7GnVPogMUiH1kvXuTV5KBQJayOdNREREzYPDg72srCzMmzevWczRA6rfNNYEXtZkZGQgI+NalU58fLzVYE+tVmPXrl3G2YIZGRkYMWIEtFotIiMjodfrkZycbHwjt2zZMrvarjj7/NRKSFXsNdNgDwC0wf74z7234T/33oazl3W48YX/4dSFHIed/4ddaXj3kXjIBU8eExEREZFtatpwTp06Fampqfjqq6+wfft2k/ddNXQ6ndURCQZBZZNWq0VMTAzuvfdep7b6rK/w8HBjZxUA0Gg0jbcYJ1Jwxp7RrrNXhNu1am9k6IrMtp8vKGlywZ6oDSfQMmbs1RjVpa1NwV4HPy/ImslMeiIiImoZHBrsNbc5ekB19ZqloeaOoFarsXz5cqSkpBjfqObl5WHTpk3QarWIjo7G6NGj611F5+zzUzNUqgMOrwB0pwG/MCDyQcDdz3SfomzgyCpAdwrIE7SBUXgASk/XrNfJQgPV2LbwITy6ZD1+O3gS7QNUOHFe/GbaVmev6LHv+FkM6h4muU9JWTne+i4RW/cfR5DGB69NvQ0FJaX4z+rfoS8sxT1xUZgZP4iz+oiIiIgAREZGYtGiRQCqZ5wnJSUhMTERGRkZyMzMRGZmpsXRAwAQFRWF8PBwaLVa9OnTB7GxsU3moVIpdYO8iIiIRlmHsylYsWckCouCvD0wq38nPP97qtlr5wtKEBWsdsXSbCZqwwm0nBl7ABAXFojFf51CQXmFxf1CVc1vfAURERE1bw4J9latWoV58+YZB5DXDvS0Wi0WLlyI2NhYR1yqWYuKisIbb7zRbM9PzYTBAOx5HbhyqPrjnDQg7zhw0wdAzZvpihJg5ytAfqb0eTya1hvHhgoNVOOHf94LoPrBg6Wb92D2kg0NOue6nYckg72ikjKMfvVL/Jl62rht/e7DJvvsOpKJhNR0rH7+HoZ7RERERLWoVCrExsYK30fWVO3l5eUZQ7HGDO82btzYoK4odVtvttT3zkpBxZ4B1eGeqJqvpbpcVIrzBSVm2/u31SBcYk6baP/GJlWxp25BLSk9lArc2jEYPxyzPNohzI/BHhEREblWg3rIbd++HaNGjcLzzz9vHEBeE+ipVCosWrQIO3bsaLFvTIiapNxj10K9GrpT1eFejdObLYd6QLNuw2mNTCbDQ8MHWKy2qy0uMgKe7ubPQazbmSZs91RaXoHx81aahHpSvt95CP/33+9QKZhPIXL87GW8/X0iPt66DyVl4jfTRERERC2ZSqWCSqVCeHi48feNJSMjA9OnT8fGjRvrfY7MzGs/l0dFRZnNS28pRMEeAFS2snacKdniqtOoYDUCvNzhrjC/TXMuv9gpa6kyGPDL6YtYsOMI/pWQhn8lpOGDfSdwOs/6/D/pVpwtJ9gDgNs7hVjdpwMr9oiIiMjF6lWxZ22O3qOPPooXX3zRoQslIhtl/CTenn0Q8O8GVJYBqf+zfp4WVrFXl5tSgY2v3odbXvwEyekXzF5PeOMRlJRVwNvDDYN7hGHyG2vxdWKKyT4nzl9BasZFREW0Ndk+7d3v8csBQXtTCau2JUEbrMHr991ucb9Pfv4LsxevR1lFJQDgnXXbsfW1B6AN9rf5WkRERETkOFqtFlFRUfjwww/rVbWXkpJi0l70zTffdOTymhSpqryKqiphmNVSpUjMbIsKVkEuk6Gdr6fZnL0jV/Idvo7iikos2nkUO86YjyjYcvICPhjeF539fSWPzy0pE25vSa04AaCLvw8CvdxxuVj8+QJAKCv2iIiIyMXs/ul5wYIFGDJkCDZt2mRSqWIwGBAfH4+dO3cy1CNqTAUSbUKqrr4RyfzFtvO04Iq9GgF+3vjp9f9Dr/Bgk+2z4gdhaC8tbunbGTf0DIdMJsO4Ib2F51i307Q6clvyKaz+M8nutbz5XSLOXdHj+NnLOHfF/M3+V78fwPQPfjCGegBw7OxldHrwLXydmILU9Is2V/1Zk1tQjGNnL6PMwiyJy7pCnLqQg/Ja63GVguJSHM7KRmGtmwkXcvOxIy0DO9IykJp+ERWVrl8XERERtU6xsbFISUmpV9Xec889Z/z9rFmzWmy1HgAoZeLbDxWtbM6eqGKvjZc72vpUzzfv3sbP7PVzBSXIsRAs2etyUSme+iVJGOoBQHmVAZ8lZVg8hyjYk6HlVezJZDIMCg2wuA8r9oiIiMjVbK7YqztHD4CxSi8qKgpvvvkmIiMjnbZQIrJRSY54e3kRUFUBHPvWtvO4t+yKvRrBGl/seWcmPty4G8fOXkZcZEdMGhZttt/I/t3g4aZEaZ2wa93ONLwy+Rbjx/O/3lavdVRUViHs/kXGj++/9TosnjUWnu5u+HF3Gh589zth208AmLRoDQCgY4g/fvjnvYiMsN4uRsqSTbvx3KdbUVxajo4h/lg55x6TlqUGgwGvr/kD89duQ1lFJXqFB+Ozpybg+q4d6n1Ne3y/8xBmfPgDruiLoPHxxPszxuDrxBRs3HvEZL9uoYH4+sVJZtWURERERI42duxYLF68GNOnT8fOnTuh1WptOm7FihVISanuCDFlyhTMnTvXmctsdJKtOFtRsKcvLUd6nWo8AIgKUkF2dR56VJAKP5+6aLZPyiUdhoUHNXgNlVUGvLQtFafzzNdR277zOcguLEWwj4fw9VxBK06VhxJKecurvhwcGoBNJ8y7vABAgKcbfNzq1QyLiIiIqN5s/olrzpw5xlBPJpMZf+icOnUqvv76a6eGeqmpqViwYIHTzk/UYlSWA4Xnxa+VXAH+fhcoMn+TKNTCW3HW5u3pjjl3xuF/T4zHfbf0g5tSYbaPn7cHbuvXxWx7cvoFLNm8BwCw52gWfjt40iFr+uLXv3HXgtXIzivAg//9DlU23PA4fTEX0z9YV+9rHjt7GU8s24Ti0nLj+R5451tj9ZvBYMBTyzfh1ZW/GSsH0zKzcfvLn+HvE2frfV1bfbM9BfcsXI0r+uqbEHmFJbjv7W/MQr2az+XWlz5BWma209dFRERErVvtKruRI0faVLm3ePFiPP/88wCqK/XeeOMNp62vqZBsxdmKZuwdkmzDee29V2SQ+H2Y1Gw+eyVn66yGegBQZQB+OiUOswAgV1BB6O/p3qC1NVX9QjTwkGgXG8pqPSIiImoENj9WpFarDLnruQAAl9BJREFUodfrjVV6NVauXImVK1dCpVKhT58+iIqKglarRXR0tMPCvqSkJCxevJgtPomknEkATv4I5ByW3udson3nbCUVe/YYN6S3MESavXg9/r3yN5PWkLX9a/LNUCrkWLJpD5QKBZ4dH4OV25Kw52iWxett3ncU1z3+IfIKS2xe4+6jWcjMzkN4sMa47fekk/j3yt9wJb8IE4ZG4tnxsfjnV79g6/5jxnNHBGtQUl6ByirTdp7Hzl5GQmo6boruhLlf/IwPNuwyu6ausARDn10G9dX2QbUFq30wfmhv/HPizVBIvBlOTb+Il774CRnZeYjprcV/pt6GNipvk33W7zmMqW9+bVPAWeOyvghRs96Du1KBmF5azLv/dgysVX1IRERE5ChqtRo6nQ46nQ7Tp09HVFQUXnrpJfTp0wdq9bWfqxMSEjB//nykpKRAq9Vi4cKFiIuLa8SVu45Cxoq9VBuCvVA/T/h7uplVxKVki4+1V1J2ns37bjl5AZN7hwtDWVHFXksN9jyUCvQOUuHvC3lmr3kqzB8KJSIiInI2u/sF1MzSi4uLQ0ZGBlJSUpCcnAydToeEhAQkJCQYq/kAmAR+ffr0QXh4uN2Bn17vmB9giVqki38B+xY6/rweLX/Gnr3+MbgnnvbxhE4QtF3SFQqPUft44ol/DIXaxxMv3XOTcXtJeYXVYA8Azufk273OxEPpmBLcFwCw4o+DeOCdb40PZLy+5g+8vuYPm9cPAN/tSMWOtAws+jZBcp+yikrhOS7pCnEoMxuFJeV4c9pIs9d1hSUY8cpnxs8zNeMizl7RY93LU43/lvz893Hcs2A1Kuo5Q7CsohK/J5/CjS/8D+v/dS9u7WteeUlERETUEDExMdi0aZPx45SUFEyaNEm4r1qtxty5czFr1ixXLa9JkGrF2Zpm7B0WzNL2c1dCq772UJtMJkNkkBqJWZdN9judV4iCsgr4ujes7WOqRECoVXsjo06b0EtFZfjrfK5wxlyO4KHGAK+WNV+vtuvb+QuDvegQPhBLRERErmfzT4QajQZ6vR4vv/wyZsyYYfa6Xq9HUlISUlNTcfDgQaSmpiIjI8MY+CUmmlYLhYeHG6v7aoK/sDBxJUVCQgJUKoYMREKntzrnvKzYM+Pv64W3HxqFh9773uZjZo8eLKxku+OGXpjzaf3+7KIj2sLLw00yGEw8lI4pN/XFt9tT8X///VZyNp+tPvvlb7PZgvZ678edmDFqIDq3a2Oy/cvf/jYLLzfsOYLVfybjxLnLWLczDcnp0i2A7FFaXoE7XluBLf9+ALGREQ45JxEREREALF++HDqdDomJiVi/fj0yMzORmZkJnU4HtVqN8PBwREdHY/To0a2mQq8uyRl7hvo9vNUcnc83f0CwW4Av5HWqGaODVWbBngHVrTxFIZutyiurcPiK+YOD/UI0uLNnKOZuO2T22uaTF8yuWVpRiaLySrN9NS20Yg8AbtIG4dOkdLMgOlqidSoRERGRM9n1qFd4eLgw1AOqK/NiY2MRGxtrsj01NRVJSUlITk5GcnKy8c1NRkYGMjIyTKr7aq5RE/j5+/sjPT0diYmJJu1LiKgW3SnnnJcVe0L/d1t/XNEX4fnPrIdy3h5ueHzsEOFrndu1QZ+ObZF02v7Q6v0ZY3BDzzD8feIcbnhmqdnr2w+lY+PeI5jy5lq7WldKaWioBwCVVVV449sELHtsnMn2T3/eL9z/3re+bvA1RYpLyzHm318iZfHjCAvSOOUaRERE1Dqp1WqMHj0ao0ePbuylNEkKubgte2up2CurrMJlwVy69n7mM9oigyXm7F3SNSjYO55TgDJBF4yoYBX6t/VHsLcHsotKTV77+0IuDAaDyb0bURtOAAhowcFeoLcHJvUKw1epmcZtg9sHoGegXyOuioiIiForm4O9vLw8TJ061e4LREZGIjIyElOmTDFuk6ruAyAZ+Gk0GruvTdRilRcBRRcBN2+gqJ7VTIFRwOUU6ddZsSfp2QmxKCkrx79W/mZxvw9mjkWg2kfy9XFDetsd7MX2jjBWmw3sHoYpN/XFyj8OmuxzOOsS7pq/qt6tK53li98O4J+Tbkb7AD8czroEfVGpw6rxaiS88Qhe/Pwn7EjLkNwnv7gUr63+A8sfHye5DxERERE5llJixl5rCfYuFIjnZrfzNe/u0VHtA283hVlVXPJFXYPWkHJJfHx0sBoKuQw3RQRhbdoZk9dKKqpwpbgMgd4exm25ErPF/VtwK04AuDcqHJHBKiRd1KG9nxdujQg2u3dFRERE5Ap2VexptVqHXNTW6r6UlGuhQ15enkOuTdTsXdwP7H8bKM1r2HmC+1sI9uSAu2/Dzt/CvTzpZgzuGY4fdqUhr86b9ECVN8YM6olb+na2eI7xQ3rjVUE4eHN0J+w8komSMvNKuRfvvtHk45jeWrNgD6ieK+dMnzw5Affe1Bef/bofuw5norTOTYfDWdk4eOq8ybbyiko8ung9ruiLsOtIJupr1IDu2LzvqNn20QN7YGgvLX6d9yA++fkv7Dl6Bl/9fkB4jnW7DmHxo2Oh5LB7IiIiIpdQSLXibCXB3nk7gj2FXIbegSrsO59rsv3wlXyczS9GqKDKzxYpgvl6SrkMPdpUV51FSDyUmKUvrhPsiSv2/FtwxR5QPf/wurb+uK6tf2MvhYiIiFo5m4O90aNHIzo62plrEVb3rVixAi+88IJTr0vUbFSWAQfea3ioBwDqjoCHP1Caa/6aux8gE7fKoWtu7dsFt/btUu/je4UHY3j/rvhp/3HjtgA/L3z29J04lJGNO177yiSgu6VvZ9x+nen14np3rPf16+vDWWPxwK3XAQAeGj4ADw0fYLbPiXNX0HPGf81agW7ce6RB1540rA++ePpOTP/wB3z2y7U2nt4ebnjt3tsAAO5uSsyMH4yZ8cAnT4zH+Hkrza6bk1+MhNR03NzHcvhKRERERI4hGew1cB50c2FPsAcA17fzNwv2AGDLyQt4qK/97wGqDAYcumQe7HUL8IWHsvphtw4SgeGZ/GL0a6sxfixVsdeSW3ESERERNSU237lftGgRIiMjnbkWoalTp2Ly5Mkuvy5Rk3QlFSi+bH0/W/i0A3zail/jfD2XkMlk+PypOzHlpr4ID9IgLjICG/51HzoEqjG8f1f8sfAhDO2lRfsAP9x3Sz+snjPRrNVL9w6BCLLQ7rNGQD2f6q3rrWkjMXPUIKv7dWnfBvfEOu5hkPAgDV6ZdDM+f3oCFAo5Fs8ai8fHDkFEiD9ujOqIdf+ciuiO5l/PCoUcT94xVHjOdTsPOWx9RERERGQZW3GKg722EsHezRFBUArC0J9OXUR5PVrup+cVoUAwOzsy6NoIhlCVeC1n9cUmH+cIZgUCgL9ny27FSURERNRU2NWKs7H06dMHq1evbuxlEDW+bHFbQfvJAZ+Q6nAv57D5y+4M9lwlWOOLL5+5S/ja4B7hSHjjEYvHy2QyxPSOsBhS3XdLPzz5j6EY+NRik7l7Km8PfPXs3fjHf76yaa3/mXornhoXY9O+APDi3cOwNjHZrGrPXl+/OAkThpo+WOLupsR/H4nHfx+Jt3p8bG8tAlXeuKwvMtn+w640vDd9NORyVqcSEREROZsopAJaT7B3vtA82FN7KOHjJr4to/F0R0yHNtiWafpgZ15JOXadvYIQH08kZF6Gn7sSt3cKQYCX5Wq5VAvz9Wr4ubtB4+GGvFLTVptn8k2DPVErTrkMUHkw2CMiIiJyhWZxN7NPnz6NUi1I1OTkHnPMebyDALkb4Nte/HqVeGYCNU2xvaXnn94dG4X/PT4efTq1w/LHx8H9apsdXy93fP/yVIy6vhsUEsFWzXZPdyXefHAk5k68ya519daG4PWr7THrq2dYEMbd0KtB51AqFBg7uKfZ9nM5+dh77EyDzk1EREREtpFuxWl/9VlzdL6g2GxbO1/LXTVGdWkn3P7a9iN44uckfH34DD5JSsesrQdwuajU4rnO5YsrBnsF+Zl83EFlvibzYM+8Yk/t4Sb5Z0xEREREjtUsgr3IyEhs2bKlsZdB1Lgqy4EcG+aTBUYDN30AeGik9/G5+gbRW6IVZ3mReDs1Sbdf11W4fezgnvjymbugUFR/q7//lutwec3L2P3OTFxaNRc3RXeCXC7Ho6MHmx07qHsYrqx5GX+99yhOffIcnh5ve6VebXPujMOcO+PqdSwAvHD3jQ6pqBt3Q2/h9lXbkhp8biIiIiKyTinxM11rqNgzGAzCGXtSbThr9AlRo73EPrVnE14pLsOmExcsnktfZv7wprdSAT930yo70Zy98wXFJu0/c4rNz+XP+XpERERELtMsgj0iQnW1ni2VdMF9AU1n6dAOuBbsSYV/Cg97V0eNqGdYMB4afr3JttEDe2DN8xPhdrVCr4aPpzsGdOsA91otf+becyNCNL7Gj92UCrx+323w8/ZAv87tEeLvi/qSyWSYf//teLpOC0+ZTAZ5rSd65XIZbugRbrLPyOu7YVKcY+b03dK3M/y8zL+uP9q4Gx9v3QcAKK+oxJ6jWTiUcdEh1yQiIiKiaxQSM/YqW0Gwl1dajpIK88rEdlaCPblMhniJqr26Dl3SW3xdX2o+X0/lYd4GNFRQsVdlAC7UaiWaJ6jY43w9IiIiItdpFjP2iAjAlVTb9vMNq/6vZ4D0Pj5XQz//roBMARgqTV/vPNb+9VGjWjr7DtzWrysOZVxEL20w7hwaCZnEzZO6AtU+SF3yBP73018oKSvH3bHR6BEW5LC1yWQyvDltJEYP7IE/kk+iXYAK/3frdSgqLcf/fvoL+qISTLqxD3p0CMKmfUex63AmurRvg0nDoo3Vhg3l4aZE/MDuWPNnstlrMz/6EcfPXcbmfUdxOOsSAGDC0Eh8+uR4+ArCQCIiIiKyX2uesSeq1gOsB3sAMLxTCFamZqKootLifmmXrQV75g+J+glm4okq9gDgjL4YYSpvAECOINizNuOPiIiIiByHwR5Rc2FrsOdXE+z5S+/jc3W2nrsf0GEYkPX7tdeUXkD7ofVbIzUamUyGO2MicWdM/eaRBvh5N6hlpi2GRXXEsKiOxo/d3ZR4dkKsyT6jB/bA6IE9nHL9p8fFYG1CCgwG05tHBoMBb3+/3WTbdztSkZNfhA3/ug9eghseRERE1HJkZmZi/vz50Gg00Ol0WLJkSWMvqUWSnrHX8oO9Cw0I9tSebrg3KhzLDpy2uF9pZRV0JeVQS1TO6csEFXvu5reEwgQVe8C1OXvF5ZXC6kO24iQiIiJyHQZ7RM2BoRK4kmbbvr5XQztLwZ5vrXYufR+tbsl56WD1MVGPAG7e9V0pUZPVv0so3n5oJJ7+eLNN+/+RfAq+E16Fj6c7hl/XFR/MHIO2/n4m+2zedxSvrf4d6dm5GDOwJ96fMRqe7gwCiYiIXCk1NRWZmZlQqVTQarUICwuz+xwbN240djtgsOccSoluEqzYs+6O7u3x06mLSNdZnoWeritEH0+N8DVRxZ5a8ABbO18vyADU/VNZfuA0fjl9EXd0CxWen604iYiIiFyHwR5Rc6A7DVQU27av/Opfa0utOGvP31N6AVEP1X9tRM3IE/8YiuLSCsz98mebjyksKcP3Ow8hNeMiti182Dhz8O8TZ3HXglUoufr08yc//4XisnJ89ezdTlk7ERERmVq5ciVeeOEFs+3h4eG49957MWPGDJvOo9FoAFRX8dvaypzsJ9WKs9JgXv3V0oiCPYVMhkAb274r5XLMvr4znv0txeJ+6boi9AnRmG2vrDKgQFSxJwj23BVyhPh4mszUq3E6rwj/3XtceG224iQiIiJyHccMLyIi58o9Ydt+gVHXfu9hoWKPFXnUir1w9zDMvedGu487dvYybn/5U1zRVz8p/cqKX42hXo1V25Jw4OQ5RyyTiIiILJg8eTJeeOEFGAwGY5vtmt9nZmZi3rx5iImJwaFDhxp5pVRDIRfffmitFXshPh6S7UlF+oRoMKGHuFquRnpeoXB7QVmFWQUeAKg8xM96d5Box2mJhhV7RERERC7DYI+oOSjNsW2/2rPxLFXsEbVy/556K54eF2P3cakZFzH+9RX46/gZbPnrmHCf+Wu3NXB1REREZMmSJUuQkJBgUmFnMBigVquhVquNAV96ejpGjBiBLVu2WDyfSqVyxbJbPUWrbsVp3n3F1jactU3v1xEvDumO+C5tha8nZeuw71wu9pzNMf7aey4HJ/MKhPv7SbSQ7+Bnf7AXwBl7RERERC7DVpxEzUGpXuIFOYCrrWt8wwDtbddesjRjj6iVk8lkeOPBEaiqMuDdH3cYt/v7eqGkvALFghkkNbanZWDQU9Kzd9btSsPhrGz0DAt26JqJiIgI0Ov1mDdvHmQymTHAmzt3LqZMmWIM6PR6PRISEvDRRx8hJSUFjzzyCObOnWtza05yDslWnC082DudV4hLRWVm29vWI9iTyWS4OSIYN0cE41JRKfaeyzV5PUtfjJe2pdp8PkdW7Pkz2CMiIiJyGQZ7RM1Bmc58m5sfcOO7wLntgJsvEH4zoKg1o8HDv3p7eZ2nMyOnOXWpRM2FTCbD2w+PwrghvbDrcCbCgzW4KyYSl/VFWLUtCfuOn8GaP5PtPq/BYMD8tds4a4+IiMgJEhMTAVybh7dlyxZERkaa7KNSqTB69GiMHj0aGzduxJw5czBv3jxkZGRgwYIFkueWyWSs3nMi6Rl7LTvY23zignB79zZ+DTpvhNrHLNizl2jGHgD0b+sPGSBs3ymikMngJxESEhEREZHjsRUnUXNQKgj2PNSAbzug211Ax5GmoR4AyBWAdrjpNoUHEH6r89ZJ1AzF9I7Ac3fG4Z64aMjlcgRrfPHkHUOx8rl7kPn5HIQFqe0+56ptSZi/9g8Ul5Yb5/7QNcWl5Sa/ysorrB9EREQEICkpCUB1CDdr1iyzUK+u0aNHY/fu3Rg6dCi++uorzJw5U3Jfg8EAvV6qUwY1lNQ8ufKqKhevxHVKKirx6+lss+2eCjniwgIbdO4IdcPnpluq2JvcO8zm8wR4uUEu0WqViIiIiByPj1QRNQdlghsM7jY8TRz5AODuB1zcV1291/uB6kCQiGwSGqjGPyfdjEfeX2f3sf/86lf886tfoQ3WYPboG/D0ePtn+rU0y7bsxdvfJ+LkedO5oXK5DAO7dsB/H4nHwO6230QiIqLWJyMjw/j7MWPG2HSMSqXCmjVrMG/ePCxZsgR6vR4rV6501hJJgptc/FxxS27FmZh5GQWCB5hu1AbBx71ht2MiND4NOh4AVBIz9gDggT4RGKYNwpn8YqRk67Du6DnJfaODNQ1eCxERERHZjhV7RM2BVMWeNTIF0P1uIO5N4IZ/ASqt49dG1MLde1PfelXt1cjIzsNzn27Bl78dcOCqmp/1ew5j1kc/moV6AFBVZcDuo1kY8+8vce4KKyWIiMg2Wq19P9vOnTsXCxcuxJ9//okpU6aYvKZW8+E3Z1NIVHSVt+Bgb9NJcRvOUV3aNvjc4fWYg1eXWqIVZ42OGh/EhgViUu8wyVaq7Xw98Ui/jg1eCxERERHZjsEeUWMqyQFSPgYOfABc2Cfex2Cof8UeETWYu5sSz46PlXy9X+d2+GCG9YqBV1b80uxaTu4/cRbTP1iH6R+sw+4jmXYdW1lZhc9//RtT3lyLFz//CbM++tHqMZf1RXj7++0AgCv6Irz4+U+Y9u53+DoxxWEtTSsrq7B86148+O53eG317ygoLnXIeZ3p3BU9nvtkC6a9+x2+33mI7V2JqFXTaDQNOn7q1KlYtWoV/vzzT8THx6OgoMD6QeQQCrkMomyoooW24kzPK8ShS+bv4zppfNCjgfP1AMBDqUB7X0/ha70CVegbYjmsdpPL4Km07ZaQv6c7busYYrY9KkiFD27viwAvd5vOQ0RERESOwVacRI0l5wiw42Wgoqj64/QtQL/HgYgRpvtVFANV5ebHs6UmkctMu/16zFv7B7LzCk229w4Pxtb//B8C1T6oMhjwxLKNkufIuqTDij8O4sHbr3f2ch3ir+NnMOz5j1FSVh1GfvrLfvz02v/h5j6dbTr+xS9+MoZ09li+dS/uuKEX7lm4Ghfzqm+2fv7r39jyVz988sR4yCXaeNmipKwc415fiZ//Pm7ctnHvEex6e0aDzutMusISDHpqMc7l5AOo/n/x9kOj8OQdQxt5ZUREjSMuLs7YRjMjI8PqjD2pc2zZsgUjR47EiBEjsGXLFkcvkyS4y+UoqTQN8ipaaMXeZgvVejIHzaPr7O+DcwUlJtvkMuCJAV3wW3o2Dl4UdH65ys/Dza51TL+uI/Sl5dhx5grcFXKM7doO/9cnAu6KpvkzFBEREVFLxp/AiBpD3klg5yvXQr0aJwRzvETVegAr9ohcyMvDDZ8+OQHyWo+ZR0e0xc/zHkSgunq+yewxN+CtaSNN9qlr0TcJqKisdPp6G8pgMOCJpRuNoR5Q3S5zwdptOJyVjc9//RvLt+41+bV531EUl1Y/hHA4KxvvrNtRr2sXlZbjxhc+NoZ6Nb787QBmLV5f72q18opK3LNwjUmoBwB/HT+LTfuO1uucrvDB+p3GUK/GvLV/GL+OikrKsGnfEfy0/3iz+NoiImqo2NhrVfQfffRRvc8TFRWFLVu2IDc3FyNGjLB+ADmEUvAgTVlly6vYK62oxK+ns822eyjkuCUi2GHXGd7JvKXnhB6h6OTvgxAfD4vHquyc8efjpsSrcb2w/q4h+G7CYEy/rhNDPSIiIqJGwoo9IlcrugTsmAuUC9r+5GcBlWWAolYrE9F8PYAVe0QuNvL67ji89Cls2HMYQRpf3B0TCXc3039GnxoXg2FRHfHz3ycw98ufzc5x4vwVfLM9FZOG9XHVsuvlj+RT2H00y2z778mnEDnzPcnjeocH47cFD2Hh139aDeDCgtS4KyYK76yzvarv46374OXuhnceHmXXE+YVlZWY+tbX2Lj3iPD13w6exJhBPW0+nyu984N5QJqTX4wDJ8/DXanAhHkrcfpiLoDqsPnPNx6GylvclouIqCVQqVR46aWXMH/+fGzcuBEajQZz586Fr6+v3eeKiorC5s2bMXLkSOTn51s/gBpMqZABdZqRtMSKvcSsK8gvM2/BPkwbBF87AzVLBrb3xxMDuuDbI2dRXF6B2zqF4ME+EQCAYGvBnpX5elK83BT1Oo6IiIiIHIePVxG5WtoX0lV4AFCSa/pxmUSwx4o9Ipfr0r4NnhoXg6k39TUL9Wpc1yUUL9w9DKvm3CN8feHXf6LKzlkyVVVVqHTg0+zWQrcFa7fV67yHMrMx7d3vsPrPZKv7vnTPjXhz2kiMHWxfoPb++p2Y+8XPNlfuVVVVYdq73+Pb7amS+2xPS7drDa5y4twV6ApLhK8t37oXw//5mTHUA4Dk9At4/tOtrloeEVGjmTVrFkaNGgWDwYCvvvoKPXv2xOTJk7F582a7z6XVarFlyxaEhYU5YaVUl5ugs0FLnLG3+cR54fb4zuYVdg0hk8kwums7fD7meqwdPxgP9e0I+dWHn0J8LD/oo/Lgc95EREREzRWDPSJXqigBzllpT1daJ9grlQgBWbFH1KTdOTQS3UIDzbanZlzEBonKsbqy8wpw5/xV8Br3L4TdvwjLt+5t0JqKSsrw8Pvfw/OOVxBwz2t489sEs5Bx95FM/J58qt7X2LTvKCqt3KBrH+CH+2+5DgDw0t032n2NRd8mYL6N4ePsJRuw4o+DFvc5eOoCdIUl+H7nIWgfeAMe//gnxvz7S+w+kom7F6yG5x2voP3UBfj817/tXmtD/LArTfK1T3/ej0u6QrPtn//6N85elp6nQ0TUUixfvhxpaWnYunUrli5datKi01414V54eLgDV0giolac5S2sYi9TV4SUS+bv4SLU3ugZ6OeydQR7W2vFWb+KPSIiIiJqfHxEi8iVsv8GKkst71OSY/qxZMUegz2ipkyhkGPOnXF46L3vzV5bsHYbxg7qabGd5CVdIW556ROkZVbPZ7mYV4CZH/6ITm0DcGvfLvVa079X/Y5Pf94PANAVluCFz39C5qU8vD9jjHEti75NqNe57fHSxJvgcbXicUC3Dhg7uCfW7z5s1zleWfErvNzd8PT4GMl9Nu07gmVbrIehBoMBq7YdxNMfb0ZZRfWcus37jmJzrdl7F/MKMO3d75CdV4A5d8bZtdb6WrfzkN3HlFVU4u112/HOw/FOWBERUdOiUqkQGRmJyMjIBp9LrVZj69at2LBhgwNWRlKUwoq9lhXs7TmXI9we36WtXa3EG8rHXQkfNwUKy8UzeFmxR0RERNR88Sc5Ilc6t9P6PnWDPVbsETVbU2/qi/+s+h2Zl/JMtu87fhZDn10Gd6X0jJL07FxkXTIP9qe9+z3GDOqBy/oijB7YA1Nu7GPTTaKSsnIs3bLHbPviTXuweNMexA/ojsE9wiTn0DmCu1KBWfGDMWPkQJPtXzx9J+6cvwq/J50yabE5dnBPjBnYAzM+/FFYBfjcp1vg5aHEzPjBwut9tHG3zWubvcS2G7kvfv4TvNyVeGzsEKv7XsjNx7s/7MDxc1cwtKcWj429AW4W/sxrO3NZJ5xzaIv3ftyJhNTTuHNoFJ4aN9QYohIRkWUqlQpTpkxp7GXYbdq0aXB3dzfb/sgjj2D69OmNsCJpboKKvZbWivN0nnlFvVwG3NIx2OVrCfHxxCnBeoD6z9gjIiIiIvstW7YMy5cvN9teVlZWr/PxTg+Rq1SVAxdsaKNny4w9uTugsNxahYgan5tSgTl3xgpDoz31DG3OXNZhyabqgO6bxBT8tP8YPn/qTigUlrtr/3rwJAqKpX9Y2LTvKDbVqlCzxSuTbsb763ciT2IOHADI5TIcXvoUgjU+AACVt/m8F5W3J35+/UFc0RchW1cAAAhW+6KNyhsA4Onuhvve/kY4V2/2kg3wdHfD/93W32R7XkExfk8ybynq4aaEQi5DUWm57Z9oHU8u3wRPdzc8PGKA5D5FJWUY+c/PkZx+AUB1W81N+45gw7/ug7en+c3Xur5oYNvPAyfP48DJ8/jp72PY9Or9Nl2TiIiap5wccYVYQUGBi1dinahir6W14kzXFZltC/Xzgl8jtL4M9vZgsEdERETUBBQUFODChQsOOx+DPSJXuZQClNvw5tqWij0PNeDCNi5EVH//d1t/vL7mD1zIdc7NtVXbkrAmIRlvTRuJnmHBuLVvZ8gFT8PXp62jNQ8Nvx6nLuRYnGF3d2wUurRvY9P52qi8jWFebZNv7IOSsnI8/P464XEPv78Onu5KTBrWx7ht076jKK8wbz314t3DsOdoFrb8dcymNUmZ+dGP8PJww9Sb+uLk+Sv4I/kUCkuqg9OwIA0OZ2YbQ70a21JOY8L8Vfjhn1MtVtGt+OMg/rXytwatr0ZCajrG/PtLjB3c07itQ6Aat/btArWPecgqknz6AlIzLiJSG4Lojm2F++w8nIEDJ86hoqoKSrkcN/QMR99O7YxfiwaDATsPZyL9Yi4G9whD53a2fU0QEZF1AQEBwoo9X1/fRliNZW6CB5EqKltOxV5llQGZgmAvQu3TCKsBgn2kHwZVufN2EBEREZGr+Pr6om1b83sqZWVlkg/qWcKf5Ihc5dwO2/azZcaeu6rh6yEil/B0d8PT42Iw59OtTrtGVZUBT3+8GQBwW78u+G7uFPjUqtCqqKzE+j32zbCzZnD3MIQGqjF+aG+Lwd6Ld93okOs9ePv1KCmrwGNLzasfDQYD7n/7WwSqfHBbv+r5g+t2pQnPc2dMJDzclA0O9gwGA/7vv99izZ9J+O3gSeNsPmt+/vs4Ji5ag69fmCRsy/nbwZP4v/9+K6xOrK9tKaexLeW0ybYQjS92vDUdHdsGWDx2wdfb8PKXvwAAZDIZ5kyIxbz7bze2f62orMT0D37A54IKw5nxg/DBjDEAgP/773f46vcDAKpbsr7zSDxmjhrU4M+NiIiATz75BAMHDrS+YxPg1sIr9i4UlqBUEFR21Jg/uOQKIRYe4mHFHhEREZHrTJ8+Xdgmf+/evRg3bpzd57Pct4vIgmnTpqF///5mv5YtW9bYS2t6DAbgvI2znkrrtOIsFQR7nK9H1KzMGDUI/Tq3c8m1fjlwAgOe/AhHz1yC7mqLzITUdOTkFzv0OuOG9AYA3N6vq+Q+/xjcE5ERIQ675qzRg/HGgyOEr1VWVWHGhz+gvKISRSVl2LrfPLjr0SEIPcOCEds7wiHrqaoyYMtfx2wO9Wqs330Y9739DSrr3PgzGAx4YtkGVNlxg1OpkOOv9x6Fv6+XXWu4mFeAe98StzetceLcFfxrxbXKQYPBgEXfJuCFz36CwWBAVVUVHnpvnTDUA4Alm/bg+52H8M32VGOoBwBlFZWYvXg9/vnVL8i6lIcqJ85WqqqqQnlFpUODUqLmZtmyZcKf2adNm9bYS3O5F154AampqY29jFZNKegqUN6CZuylS7S9bJIVex58zpuIiIioueJPclRvzWmWQ6MrzTUP7KSYzdgTtOJkxR5Rs+Lj6Y6Nr96Pd3/Yge2HMlBWUWHTcW4KBbp1CMSXvx2wvnMtR89cRq8Z7wIAbu3XBX5e4ps6AX5eVgO/W/p0xm9JJ82233FDLwCAl4cbJgyNxHc7zG+UvnTPjXat2xbPjI9FUWk5XhW0qky/mItVfyZB5eWBYsEMvXFDqtfcv0t7eHm4Cfep7Za+ndG3UzvMvecmLN6021i55ghfJ6bA012JT54Yb2xXefDUeRzOuiTcPyoiBCnpF822vzrlFvTr3B6/zHsQSzfvwca9R2xu+7rrSCb+SD6Fm/t0llhjMioFN1vf+j4R3h5uuJCbbxLYicxfu014jprX5q/dhiC1D16eeBNmj7nBpnXb6pvtKXhq+Sacz8nHDT3CseyxO9Bb67igmai5cPQsh+ZsxYoViIiIQGRkZGMvpdUSzdiraEEVe6L5egCgVTdOxZ7lVpys2CMiIiJqrhjsUb01p1kOja7gvHi73A2oqnNjuTQXMFQCMgVQVSGey8eKPaJmp62/Hxb+n7jazJqeYcF48fOf6nXsrwdOCLe3UXnj3Fcv4JKuEB0ffEs4j65jiD/++0g8+j72gUkV2Yj+3Uzm5s2MH2QW7I0d3BPXd+1QrzVb8/LEm1BcWo5F3yaYv/bFzwjxF/87NP5qlaG7mxIj+3fD9xbmDsb00uLn1x80fvzi3TeiuLQc89Zua9jia/nytwPwcnfDR7PGQiaTSc5BnHNnHF66+0Z0eegtXNZfu2HY1t8Xs+IHAwD6dW6PZY9Vt25IPn0BN7/4P+QWWK/SXLB2m2Swt26nuJ0pAPxn9e9Wzw1Uh5XWXNIV4ollGxER4o/RA3vYdF5r/vfTPkz/4Afjx7uOZOLGFz7GHwsedmgVKVFz4OhZDkQNIWrFWdGSKvYEwZ6bXIZQP/sq6x3FUitOX87YIyIiImq2+JMc1VtzmuXQ6Aolbmy26Q1cOmi6zVAF/DoLUHqYV+/VYLBH1Ko8Oz4GZ6/o8fHWfSirqMRDw6/HqAHd8eSyjcjIzqvXOccO6gmlQoF2ASpMu70/lm7ea7bPhKGR6K0NwcePj8czH29CXmEJbuvXBUtn/8Nkv5uiO+Hth0bhn1/9gqLScgzv3xWfPjmhXuuyhUwmw+v33YaNe4/gUGa2yWvncvJxLiff7BhtsAb9Orc3fvzchFiLwV5sZITZtn9PvRVFpeX47w82zkytRamQo0Iwc2fZlr3wcnfDWw+NFM4F9PJww8sTb4KPpzu+fnEypr37HU5fzEXv8GD874nxUAtu2EV3bIvfFzyEB975BkmnLVfp/J58CrsOZyIl4wK+33EIgSofvHTPjfD2cMPfJ8/Z/Xk2xCtf/YL4Ad2N8/vsYTAYsHJbElb8fgC/SITZOfnFuO3lT7Bt4cPIyS/GO+u2o6KqCjNGDsLw/tItZWtc0RfhtdW/Iy0zGzf0DMcz42Og8pa+YUqmKiur8MVvf+O7HYcQ4OeFF+4axgpKF3H0LAeihhC34mxBFXuCVpxhKm8oBIGmK/h7SlflNdaaiIiIiKjhGOwRuYJksNfLPNgDgIIsy+djK06iVkUul+O96aOx8IHhcFPKoVQoAABjBvbAxbwC7D9xFv/4zwq75ohNGtbH+PvnJsThfz/9ZRY8TRwWDQB44NbrcP8t/VBSVgEvD/ENoifvGIrHx96AotJy+Eq0/nQkuVyOF+6+Efe+9bVN+08a1sckMBrYPQy39O2M3w6atxkFgBjBHD6ZTIY3p41EUWk5lm0xD0JHD+yBPUezcElnelMvUhuC92eMQfyrXwjbf7774w4E+HkhrU5ICQDDr+sKH8/q6vhhUR1x4pNnUVBcCh9Pd4sBWHTHtvj7g8dwWVeIoqvX/PSXv/Da6j/MP9fnTGfjbth7GA8PHyB5bmdJOn0Bm/Ydtbtqz2Aw4IXPfsJb3yda3Tc7r9DYprbG+t2H8Z+pt2LuxJskj8vMzsONL3xsDNJ/SzqJv46fxcZX76tXENka/feHHXj+s63GjzfuPYJDS55E+zb8mYaoNRG24qxsGcFeRVUVzghanHfUNE4bTgCQ898oIiIiohbJ/HE5InK8QkHVg8IDUIvbn1nlzoo9otbIy8PNGOoB1UFTW38/xA/ogT3/nYl2AX42nWfC0Ejc0vfa95+IEH+8N320yT7PjI8xqXCTyWSSoV4NuVzuklCvxt2xkejcLsDqftpgDebcGWe2fe494iBHLpdhSM9w4WsymQwfzhyDx2rNg1PI5Xhl0s344Z9TkfTh4xjQNdT4mp+XB7585i4Mi+qIdS9PhYeb+JmqV1b8Ktw+7mr70Np8vTxsDpMC1T4ID9YgPFiDp8fFQGOhJVeNguIyu6sS3ZQKbPjXffjvw/F2HVfX3C9+xvy1f5j8WvD1Nny/8xAqKs3bxQLAv1f9ZlOoZ8krK37FO99vF7527ooet8391Kw6duv+Y9i076jJtsrKKuxIy8Cb3yZg/to/sOibP/HT/uN2he5SDmdl4+Ot+7B1/zFUCqo/a8vOK8Bnv+zHN9tThG12Xa2krByvrzENlfVFpXhb4v85EbVcboKKvZbSivNsfrFwXqBW7dMIq7lmYHt/s23XtdW4fiFERERE5DCs2CNyBVHFnndbwKuN+XZbePDpdiIy1b9LKFIWP4Gt+48h+fQFvCGYPwcAN0d3whdP32m2fcaoQbilT2fsPJKJHh2CMKh7mLOX3GBKhQJz7owzmaVWV7sAP/wy70Fhy8q4yAgM7aXFjrQMk+0Dunaw2GJRLpfj3emjcf+t1+H4uSuI1IagV3gwACDE3xc73pqBrX8fQ15BCUYN6A5/3+q5Orf164KvX5yECfNWCttymn9+cowe0N3qfrZSeXvisbE3CKv2bBGs8UG2oMWYQi7HyufuxqgB3VFUUob5X28zq1qsfY71r9yH3Ucy8eTyTWavp2ZcROpXF4XHDukZjg3/ug8a32tzipZs2l3vz6eu5z7dAi8PJWZenVsIAGXlFRj1r89x4vwV4TEL1m4ztg81GAyYtfhH/O+nv8z2GzekN1Y9dzfcJYJda9YmJOP+d741hnRjBvXA93OnQC64Qb73aBZGvPI5dIUlAICoiBBs+Nd9CAvS1OvajvBb0knkF5eabf/k57/w1kMjWfVI1IqIKvZaSivO9Dzz+XoAEKFuvIo9ALi9Uwj2njMd8XBrRHAjrYaIiIiIHIEVe0SuUCiYceTTDvC0XmkixIo9IhLw9/XCpGF9sOCB4Uj/7Dl0DDF9QjumlxY/vHKvZOVd19BA3H/Ldc0i1Ktx3839MCyyo/C1YI0Pfpn3IDq3Ez9EIZPJ8M7Do+Dpbhq2vHD3MJuu3a9ze9wdG2UM9WooFHLED+iBKTf1NYZ6NUYP7IF5991u0/lv7tPZJMRyhMfGDDG29rTXS3ffiLcfGmWyTS6X4bOnJmDC0EgAgLenu1n1Z23vTR+DAd064LGxQ+yu7tt5OBM9Z/wXe49m4XyOHpd0hZjz6VbrB9ph9pINeP7TrTh65hIqKivx8dZ9SEkXB40AsPtoFpZs2oP8olJ8v/OQMNQDgHU7D2Hym19LVh1aUlFZiTmfbjWpvNuw5wg+/WW/cP8XPvvJGOoBQEr6Rdz+8me4kGs+e9LSNS/mFlitDLTVup3m8yMBIL+4FAccPMuxuLQcOflFDlu7LaqqqpCdV+Cy6xE1Z24K81sQlQYDqhxQ2dzYTks81BKhadyKvWHhQXhqYBd09fdFJ40PZl/fGbd14oxTIiIiouaMFXtEzlZeCJTpzbf7tAU86hnQ1fc4Imo1woI0+PuD2Xjj2wScPJ+Dgd064NHRg+tdMdRUubsp8f3LU/DRxt34M/U0ysorIZfJ0FsbjBfvvtHq/K7ru3bA7wsewuKNu1FlMOC+W67Dbf26OHXNI/p3M5k1JmXckF4Ov3YblTdmjBpYrxaId9zQC2FBGlzfNRRLN++FUiHHk3cMQd9O7U32uycuGt4ebliTkIyzl6v//esQqMbEYdEm8/MeGn69xeo+key8QtzwzFKb1ztpWB/cfl0X3D73U+w7ftam4976PhFvfZ8IjY8n8moFZFIeW7oBjy3dYHW/dTsP4YF3vsMXT98JheDGtpS0zEs4c1lntn3uFz/jgVuvM2nPm1tQjIRD6Wb7Hjt7GbfP/RS/L3gIgVZawm3YcxgzPvwBF3ILEBHij8+emoA4ifDcFhWVlVi/57Dk69/vPITruoRKvm6Pr34/gGf+txlX9EXo07EtPnlygklLYWf4ZnsKHl+6Adl5hYjUhuD7l6dIPkxARICboGIPACqqDHBXNO/q3Uyd+Xw9T4UcIT6ua1MuZVSXdhjVpV1jL4OIiIiIHKRl3d0jaopEbTgBwLcdIHcD3FXi4M8Sd9+Gr4uIWjyVtydet7E6rDnT+Hph7sSbMBfimXnWDOoe5tIqxV7hQQjw80JOvvkNwBoymQz/GOT4YA8AnrpjKD7csBul5RU2HxMXGWFs5RjTOwIxvSMs7j9mUE+MGdTT4j7enu54ZdLNNoVi9vjsqTtx3y39TLZt/s8DuPWlT5B0WlBBL8GWUM9eq/9MgpeHEstm3yFsoyly4JS4ou2yvghfJ6Zi8o19jNt2pGVIzvM7lJmNEa98hl/nTZOsBP1uRyomLfr/9u4+Pq6yzv//O2l6S8kkhUJpIFO5E0omqYAgoRNFUVuSraIorQnqV9YGY3EVvkmQ0dV1mULSXXZX21nSr6zuNpGg+9t1IbHd1V0lU1vFG0imxJsV7EypVIF2Tiil9/P7I8yQyZxJZjK3Z+b1fDz62DlnzrnmyjoXJyfv87muR3Xq9fWu9v7xkG7Z+C396qHP6azymU0lN7Rnr14eM5+eThqv5kvHf6d+8/yLWv/Vf9fx1ysbh39/QDfe+7B+sPH2jIV7w8+9oI/+zXcin7nH/0c1fflfNLz5zoJ7iAJIl7I4/+07cfq05iTx0EM+etlkyuGlZ85XKdMNAwAAIM2s/ZszYAXxgr0zXv8jU7LTcc4pl0pmTX8cACAvlZaW6vrl9imPWbncrnMrM/MQx3mLyvWJ91yV1DmdH0psetJkfarxWv3lundq8TRVZIl639sujwn1JGnRmQv0ffft+ui73qLZZbN0dvkCbW5bo+5PrErL5ybjn/7rF5q95ou69nMefan3Bzp6/MSUxz/9bJzfIyTd9jff1sr/26OvPbZLp0+f1k6Tar2Jnnr2BTV+6Z/1ypE3/vj82E9/pfd9ZZtmNbn04fsfiYR6YS+PHdHmx3dLGg8O3/eVbXrnPV+XZ+AnptNd/nDkOTV+6Z91470Pa9v/PKV/2/XMlH369fMvquJDX5G7/4c6nkTYPNk3vv+LSMAWFnz1qN77xW/oGX/sdKoHXzmiz/YM6MZ7H9ZnHnp8yvAxnvv6fxjzmb/d/5K2/fDppNsCioXZGnuSdPKU9afiPHQ09r/nZ82f2fTXAAAAwFR4lBTItMNx1o454/WpUOZVSmN7zY855yrpT5PW0FlcZ34sAMAyGq54kx7/6a/jvp+JaTgnav+gUw//589jQgkzV128VO+98pKM9KOkpERfan6XvrjuBh06HFsht//lMd30pW/qhYOJrQ93763viPveWeUL9I3P3aJvfO6WqP1Hjp3Ql/v+O6l+p8PP/3e/fv6/+/XjUb8e/9JH4659+XScir2w3b8OaPevA3rquRf0q8Cfpv3cn/xmn973lW0a+PJH9cSe3+uD7j6dPj31H9S/9vhuvfvKi7Xqi9/QkWPjf7h+Ys/v9drxE7r7A87Icb/83X695wv/FGnvhyPPTdsfaXytvb/s/YF2/Sqgf/9Cc9LVbqFQKG6A+PLYEa35yjYNb75TC+ePT4e3/yVD77jn63ruwMFIPwd/9hs90fVJnX92YtOd/2rfn+J+Zvd3hvSxd70lappUAONmT1GxZ3WHXjses69invl/2wEAAIBUULEHZJpZxV5JqbTgnPHXc+NU7JUtkK51SWdd8ca++Yuly5rT30cAQFY5a5ZN+f7N110x5fupsp9TqW987haVJDA92Oc//I6EjktFaWmpzipfEPOv9k1L9F/3fUJvOrdy2jbe/ZaLdfUl5yf92V9Ye4P+qvldCf2M11x6vv574+2aUzZ9YPN3n2zU/9z/55o3Z+qQ6ocjz+mDG/uipkY9fuKken/4tP7237wa2rN32s+SpH/+wS/15G+fT+jYJ/b8Xh9w9+lzWwenDfWk8bX7nO1bI6Fe2P3ffkInJoTD7kd/lFB78ez4xW/V9OV/0Rf+5b8i//5x8Cdx12F84eCYPAM/0d1f/56efeFg3Hb3/vGQtm7/mSTpj4cO691f+KdIqDfxmHe7/kkHDiUWIj/w7Sfivve7F17Wd3buSagdoNjErdhL4b8d+eC1E6d01KSKeREVewAAAMgAKvaATHvVZD2f+edIpa8Pvzlxph+zvUkqmyc5u6SXn5FOHZcWXSbNTs90ZQCA3HnLRefFfe/qS6pUfU5Fxvuw9u21ekftm7TrVwFdedFSbfjHx7T957+NOuaK6nP0vrdNvVZepi2vPkdPb75TPxx5Tr/a96I+/83/ND1uqmq9qZSUlOgL696pW1Y69CPfc/q057G4x95cf4XeUXuh9m+7R/89/Kx2/Py3+uYPfhlz3GLbGfrz916tBfPm6N9cLXr/X2+bsjryP3/xv/pI96Pq71yrl185opv+8ptJrQc43c9ntu7e95/6XcptHzr8mp7Y83vduOJivXr0uL67ezTlNv97+Fn99/CzUfv++pEf6ntf+ZhWXPjGWnmP//RXuu1vvqNXTNa0MvO3/+7V7e+9Wjd96Zv6zfMvmR7z2/0v6c++/C/6yYOf0qwp1vp67sBBPfLEyJSf98C3n9CtTkfCaykCxWL2rHjBnrUr9g4eja3Wk6TKeQR7AAAASD/uNIFMM6vYO2PJhI04w3Bh1fj/LSmVznZI515FqAcABaJs1ixddv5i0/fef11mp+GcaEnlmfpA/RVadm6l/qrlRs2aEELMKi3VV+/4s7wIJhbOn6s/u/ZyddzSoBd6P6/l1edEvd9ywwo11Lwppc+47ILFuuOma7XxY++Je8zNr/9vs+jMBfrQSoce/uwH9d8bb9eCSdNo/t36Ri14/Y+5773qEj36+XWaPU2V33d3j6r641264GNdaQv1JOmf77pF5y06M23tTfbtIZ/+8PKY/r8fT72WXir+GDys937hG/r+U7/TL363X488MawP3/9IwqGeJB04dFhv/ewWPf1c/DULJemXz/5B/UMjOvjKEf3h5TGdNgkbvr7jZzFrEU62x/9HPTbFdLtAsSqLOxWntSv2DsUN9piKEwAAAOlHxR6QSadOSK+ZPBV+xoRKjcW10rPfjT3GHv8PiwAA6/vIO+r0l70/iNn/gfrMTsMZz1UXV+nJv/+U/nHwpzp1OqRPvOcq1V9uz0lfpnJOxUL95MFP6e+/+2P9at+Lun55tT7xnqvT1v6nGq9V978OKfhq9Jp/jmXn6pKqs2OOf0fthRre8hn9w3/skvHqUa17e53ee1X0moRrrr1cP3nwDj30vSf1w+Hn9LsXXjb97D8FzaecnOzGt1ysp579g14eOzLlcQvmztaHnQ5defFS3XDP1+NOaTnRB6+vke2Mufqn//rFtMdK0sP/9XM9/F8/T+hYSfqw06G7bl6pr//nz/T9p34n/5+CCZ330tgRrfriNxL+HDNTTdc50Uf/9juaUzZLx0+e0nWXVesbn/tg1P/2kysK47n/2z/S+952ecansgWspNDW2NvzoqHfvHxYv33ZfBpfKvYAAACQCQR7QCYdOSDJ5OnThW9MJaVzrpTmlEvHx97YV3np+LSbAICC9fEbr9TGb/9IR4+/sbba6qsv1ZvjVPJlw4oLl6rnzptz9vmJOmPeHLnW3pCRtssXzNO9t75DHf+0I2r/nWvq455z4ZJF+ofWpinbXXHhUj204f2SpF/+br9udP2TjEnhYSJsZ8zTjq98XCUlJRp48tf6oLtPJ03WdZKk6y6r1uyyWbr8gnP0n3/9f/TOz389JrCc6Ot/8QH9n3dfpVAopGf/cFBP7Pl90v2bzs31V+itl56vt146vh7i7l8F9N4vfkOvxql2yZXw1Km7fx3Qu+59WD/q+qQuXLJIrxw5pqeenbrqL2zfi4b2vWhkZWpdwCpmx1tj75T1Kva+86vntfWpqf87uWg+FXsAAABIv9zPrQQUMrP19aToir1Zc6Tr3VLFxeNTbZ57tXR1+/gUnACAglV1tk3/5mrRZecv1sL5c3TLyhr9v8/kf6hWDD73/ut1z4ferooz5ums8gX6cvO79H9uvDJt7V95cZW+91cf08L5yVdyrHjTeZEKsKZrLlNf+60qjfOHcmfNssjrugvP0/avfFxnzp9remz14gq13LBC0vi6fP989y2qXlyRdP+mMnd2mVZfdWnUvusur9b/3H+7rr6kKm2VbbNKS7U1wbF00XmLpj1m/8tjuuTP/1aL192ni//8b0yn4bz4vLMiry9YbNNXW5v07MP/l1APmKQszn+vrFax99qJU+rbE5j2OCr2AAAAkAlU7AGZdORP5vsXnBu9XXGRdMNXM98fAEBeee9Vl+iZqz6b625gktLSUrk/9h65p1hvL1Vvu6xaj/3lR3XTl74ZVbU5nRUXnRe1fcvKGh09cYs+/uC/KhR6o+KlpKREN18XPa3rNW++QANf/qhW/+U3deTYiaj3Oj7UELUO4AWLK/SDjZ/Q2zv/n144aD7FXLLe/ZaLdeaC2GDx6kvO10//rk3HT5zUqdfX2Tp24qQ+6O7Tj3zJVw02XfNm/Z8br9RX/2OX9vj/GPe4qy+p0mfff71aNn07oXYPvvJa3Pce/uwH9IV/+b4++q63qOWGFZozm9sswEy8NfZOWmyNvdGXxvTqiVNTHlNWWqKFc/hvAQAAANKPkiAgk+IGe+dktx8AACDvvN3xJv3HX96ms8oXJHzOWy5aGrOv5YYV+uZdt6j89dCstLREf7nuBtUsOzfm2JVXLNNjX/qollQujOz7sNOh1lVvjTn2ovPO0g/ct+vyC9IzPezN06wfOWd2mebPna35c2erYuF8ffeLt8WsVzjZh5wOvcPxpsj2hUsW6e/WN6q0tFRf/4sPaP7c+NPgff7D71DDFcuS+hnMLJw/R2+77AL9qOuT+sR7ribUA6Ywe1acqTgtFuy9eOTYtMdUzputUtbYBAAAQAZw1wlk0msmwV7ZfGn2wtj9AACg6Ny44mL9Zutd+vGoP7LmXtVZ5Xr+5TF97G+/E3VsSUmJrl9uN22n5YYVes9bLtYzgT+p6qxyXVp1dtzPvKH2Qo0+9Dn9/H/3a9GZ803DwrDLLlisX3x1g37y632aVVqiF41XdcvGbyX9c84um6U/uya59YPPXDBXg1/+mJ787fP63R9ejnpvVmmpHMvO1RX2cxUKhfTkb5/Xq0ePa+VyeyRYe+ul5+u7X2zRmr/apmMnoqsir6g+R2uuvUylpaW6cMkiPXfgYNI/U9h1l1WrbNas6Q8EELdiz2pTcSYS7FUwDScAAAAyhGAPSLdQSDr6snT6lHnF3vxzJJ7cBAAAr6tcOF9Nk0KvU6dO69tDIxr82W8i+1puWKELl8RfE+6cioU6pyKxh4dsZ8zTu1ZclNCxc2eX6e2vV8WdPn1aziuWyfvM3rjHnzFvjhbMna0XjVcj+z7/obcnVZkYVlJSomvffIGuffMF0x5j5sYVF+vbn1+nD9//SCTcm102S1va3qfS1wMGZ82ylIK9hglrGQKYWrw19qxWsffC4aPTHrOIYA8AAAAZQrAHpNPJo9JTX5We/1H8Y5iGEwAATGPWrFL1d67Vv/zPU3rq2T/oqkuq9OfvuTrX3VJpaam+91cf09YdP9OzL7ysa958gVpuWKEnf/u8vvWjYS2YO1ufbnqbbAvmafPAbj3/kqF31l2kD0wzDWcmNV1zmZ7efKd6vvekTpw6pY/deKWuurgq8r7zimX65x/80vTckpISXX7BYo0G4kyvrvHpTWENTqdTNTU1ue5GUZtdIBV7BxII9irnxZ8KGAAAAEgFwR6QTv7vTx3qSQR7AAAgIQvmzdEdN12b627EWDBvjj77/uuj9plV1X3+w+/IYq+mdmnV2frbT95k+p5zimDux3/TqmvffIFuvq9Xj/3kV6bHXHPp+enoIrLgkUceyXUXit7sOBV7J04VXsVe5Xwq9gAAAJAZ5o/LAZiZF3ZNf8z8xZnvBwAAABJy0XmLdN6iM2P2V51VrrdeMl7Zd2+ckHLlcrvmzaEqB0hUvDX2TuZhxd6JU6f10pFjOnw8eo3O46dO66XXjk97PhV7AAAAyBSCPSCdXhye/hgq9gAAAPJGSUmJPux0xOz/2I1XRtbhe+ul52vVVZfGHHPLSqZ1BJIxe5Y11th77tCratvxlNZ990mt+/ef6ru/+UPkvUSq9STW2AMAAEDmMBUnkG0EewAAAHnl3g+/Qz/99T795Df7JEkNNcv0+Q+9PeqYhz/7Ab3z81/Xb55/SZJ001vfrPWrr8l6X2EdPp9Pw8PD8vv9MgxDNptNlZWVqqmpUUNDQ667lxPxKvbybY29r/38d9prHJEkHT11Wp5fPKurz6vU+eXzE1pfT5IqqNgDAABAhhDsAely+uT0x0gEewAAAHnmbNsZ8m5ar6eee0Elkt5y0VKVlERXFi2pPFN7PH+hn/7meS2cP0c19nNjjgEkyePxaPPmzTIMY8rjmpub9elPf1p2uz1LPcu9eGvs5VPF3ktHjmnPi2NR+0KSdjx3QH++4k164fBrCbWziDX2AAAAkCFMxQmky7Gpb9wlSSVl0rxFme8LAAAAklJaWqqrLq7SlRdXxQ3sSktLdd3l1XIsW0Kohxh+v1/19fVyu93ThnqS1NfXp/r6eg0MDGShd/mhLE6wl08Ve3+IU5H36OjzkpiKEwAAALlHsAeky7Hg9MfMP1sqYdgBAAAA+WZsbExXXHHFjM71+XxavXq1/H5/0ue2trYWTbgXbyrOfKvYm0qiwd6C2bPS0R0AAAAgBgkDkC6JBHtMwwkAAADkpWAwqLGxsekPNOH1eqOq9Jqbm7Vjxw6Njo5q//792rFjh3p6euR0Ok3Pb21tTajKz+riVeydPJU/wd6fXo0f7IVCoYSDPap6AQAAkCkEe0C6EOwBAAAAljXTUE+S9u7dG3m9Y8cOdXd3y+FwyGazSZIcDoeamprU39+vnp6eyP6JNm/ePOPPt4rSkhLNMgm88mkqzj9NUbEXPHZCBxIM9gAAAIBMIdgD0iWRNfYI9gAAAIC89Nhjj8343EAgIEnq6uqSw+GY8timpiZ1d3fH7B8cHJzx51vJ7FlmwV7+VOz98dX4wd2eF8d09FT+hJAAAAAoTmW57gBQMI4dmv6Y+QR7AAAAwEzdcccdKVXWxeP3+yPh3EwEAgHZbDa1tLQkdHxTU5MaGxujwjy/3y/DMEyr+QrJ7NJSHVV0OHYynyr2ppiK82d/SOCeT9Itl1WlqzsAAABADII9IF2o2AMAAAAy6vTp0xoaGsrI+mWhUGjG7fr9fjU2NiZ1zpo1a2Kq9AKBwLQVf1Znts5evlTshUIh/XHKYO9gQu2858Jz09UlAAAAIAZTcQLpkkjF3oLFme8HAAAAUKBcLlfkdSgUSuu/mTKM8Qf81qxZk9R5hR7gxVNWGvtniHyp2Dt84qReO3kq7vsvvXbcdP/7L12qZbYFuvysM9X9TofeVHFGproIAAAAULEHpM10FXuz5jEVJwAAAJACu92ulStX6sc//rG6u7t1wQUXpKXdQCAgj8czo+k4bTabRkdHk55C0263x+yrrq5O+vOtZrZJxd7JU/lRsTfVNJzxLCibpbarLsxIFSkAAABghmAPSJdjwanfr36XNGt2VroCAAAAFCqXy6XVq1erpKRETqczbe3abDZ96lOfmvG5yQpX+qXajtWYT8WZHxV7U03DGY/dtoBQDwAAAFnFVJxAOoRC8YO9ikukS2+VHOuz2iUAAACgEDkcDtntdm3ZsiWt7S5btiyt7U1neHg4ajvZNfqsynwqznyp2Dua9DnLmHYTAAAAWUawB6TDiVel0ydj97/5VumGf5Cu+BjVegAAAECaNDc3y+/368c//nHa2qyurk5prb1k7dmzJ2r7zjvvzNpn55LZVJwn8iXYO5J8xd4y24IM9AQAAACIj6k4gXSIV603tyKbvQAAAACKQktLizZu3KiNGzdqcHAwLW2Wl5erubk5LW0lYvPmzZHXTqdTDodjRu2Mjo4mdXxVVZWqqqpm9FnpMHtW7PPFVp6Kc1kFwR4AAADG7d+/X/v370/4+GR/lw8j2APSgWAPAAAAyJry8nJ95CMf0b59+9LabldXV1rbi2dgYCBqjb2enp4Zt+VyuZI6/q677tLdd989489LldkaeyezHOy9/Npx7fmTobPmz9HlZ5dr1ut9+tNMgj0bU3ECAABgXH9/vx588MGMfw7BXhYYhqHHH39cFRUVKi8vV2VlZdT75eXlkddjY2OR14cOHYpsh9eRQJ4i2AMAAACyqru7O9ddmLGNGzdGXnd1dclms+WwN9k1O8dr7D39x6C+NDSqIydOSZLqzz9LX7j+Ms2eVZr0Gnvlc8tUOY8lFwAAAJBdBHtZMDw8rM7OzpTacLlcamtrS/j4oaEhDQwMaGRkRIFAQIZhyGazqbq6WmvWrFFzc3NR3TxmHMEeAAAAgAS43W75/X5J42sFtrS05LhH2WVWsXfiVPaCvZ5fPhcJ9SRp1/Mv69u/el4fuvx8HTx6Iqm2ltnOUElJ7M8DAAAAZBLBXoHx+XxqbW2N3Cg6HA7V1tYqGAzK5/NF/rndbnV1dRXdTWTGEOwBAAAAUxobG9O3vvUtrVy5UjU1NbnuTk74fD55PB5J4+vqpaPq0O12a/ny5Qkfn8v19SSpzLRiLztTcY4dO6HfHXo1Zv8jz+zT5WefmXR7y2ysrwcAAIA3rF27Vk6nM+HjR0dHk55aXyLYKyi9vb2RysDm5ma5XK6oqjzDMNTe3h5ZXL6zs1N+v39GXxxMYhrslUpzkr85BAAAAArR448/rvvuuy9S4eRwOFRdXS273a5ly5Zp3bp1Oe5hZhmGoVtvvVXS+M/e39+flnaXL1+ua665Ji1tZcNs0zX2slOx9/zYa6b7j506rS8+MZp0e8sqCPYAAADwhqqqqqw8SEewlwUT19Rra2uLLJIeDAYj+yeurTf5PUmqrq6e8jOGhoYioV5bW5tpWGez2bR161atX78+Eu55PB7V1dWpqakp4Z8HJsyCvbk2qST2aVQAAACgGPX29kqSQqHxEGdkZEQjIyOSpJKSkrQHe/fff78aGhp0/fXXp7Xdmbr11ltlGIbsdrseffTRXHcnZ8pmxd4jnchSxd7zr5gHe5J0/JR5H64//yz9+PmXTd9bZjsjLf0CAAAAkkGwl0U2my1j1XF33HFHwp+xadOmSLAnSR0dHQR7qTpmxO5jGk4AAABA0viDjD6fL1Kt19jYqIceeiijnzk8PCyPx6Pu7u6cVwOuX79ePp9PNptN27dvL+r1znNZsbcvTsVePBVzZ6vtqgvjBnt2puIEAABADlBOlEUVFRUZadfj8USqADds2DDt8TabTW1tbZFtwzAiT89iho4eit03t3hv1gEAAICJvF6vpDeq9TZt2pTxz+zq6lIoFJLb7dYrr7yS8c+Lp6OjQ4ODg4R6ryszCfayVbG3/5UjSR3/ngvP1TlnzNP1558V896SM+aqfO7sdHUNAAAASBjBXgHYvHlz5HVjY2NC56xZsyZqe2BgIK19KirHX5Feeyl2/7zK2H0AAABAERoeHpY0PuVmc3Ozzjwz82tR2+12feQjH1EwGFRHR0fGP8+M2+1WX19fJNSz2+056Uc+mV0a+2eI0yHpVBaq9qaaitPM6ouWSJJuc1THVBquuXRp2voFAAAAJINgL4vKy8vT3ubAwECkWs9msyV8o+hwOKK2w0/QYgaefUw6fTx2/4Il2e8LAAAAkIf8fn/kdUtLS9Y+9wtf+IKk8fumffv2Ze1zpfE1BT0ejyTp0UcfJdR7XZlJsCdJJzNctXc6FNL+V44mfPyKc206v3y+JOmiyoVyv6NGV59XKcc55fr0VRfplsuqMtVVAAAAYEoEe1mUiak4h4aGIq9ra2uTOndyuDexLSToxBHp2f8wf29pfXb7AgAAAOSpQCAQeV1TU5O1zy0vL9dNN90kSVldfqC3t1ednZ2SpEceeSTm3quYma2xJ2V+nb0XjxzT8VOJh4eNF58Xtf2WJRW6/4YaPXhjnd7/5qWR9SIBAACAbCPYy4JDh0zWX0uTiVNoVldXJ3Xu5CBw4s02EvT7QenE4dj9514tVVyU/f4AAAAAeSgQCKikpEROpzPrn/2Zz3xGoVAoUj2XaQMDA1GhXkNDQ1Y+1yrKZpkHYpleZ+/5MfNpOBcvmBOzr2LubNWbrKsHAAAA5AOCvSxK91SchmFEpuGUlPQi7MuWLYvanjg9DhJw+qT0u++av/fmW7PaFQAAACCfhe9bkn0YMR0mVgju3Lkzo581NDSk1tZWSVJPTw+hngmzNfakzFfsxVtf7+5rL9WFFWdE7fvctZdoziz+XAIAAID8VJbrDhSTiVNx+v1+9fb2yuv1KhAIyDAM2Ww2VVdXa82aNWpsbJx2DYbJFXbJrtkw+aba5/MldX7RO/hr6ZhJNebZDumsK7LfHwAAACBP2Ww2jY2NJf0wYro4nU7t3LlTXq9XK1euzMhn+Hw+rVu3TpLU1dWlpqamhM81DEPBYFCSNDY2VtBTd5bFmYrzRKaDvTgVe5effab+9sZa/dD/ol46ckzXX3CWLl10Zkb7AgAAAKSCYC/L/H6/Ojs75fV6Y94zDEM+n08+n09ut1vNzc3q7u6O29bw8HDUdrJr+KW7grDovLzHfP8lt2S3HwAAAECeq6io0NjYWM4+v7q6WqFQKGMPM/r9ft166xuzdvT29qq3tzfqZw4Hd5KiZl6ZzGazaXR0NCP9zAdlcSv2MjwVp0nF3qL5c7Rg9vifRf7skvNi3gcAAADyEcFeFoRv5gYGBtTX15fweX19fRoZGdGjjz5q+mRrqjfGlZWVUdsTbzSRgJeeid1XOltaXJf9vgAAAAB5LPxQ4VSBViaF76cysfyA3+/X6tWro342ZkOJb3a8ir1TmanYO3n6tJ46YOjnL8TOtnL+mfMz8pkAAABAJhHsZVH4Rs/hcGjNmjWqrq5WeXm5xsbGNDQ0pIGBgZgbXZ/Pp9bWVvX398e0d+iQyTSQSZhcsZdsUJjsU6RVVVWqqqpK6py8dfqUdNDk5698szQrdvF1AAAAIJP279+v/fv3J3x8tivCnE6nfD5fzh8mTPfnG4YRE+phavHW2DuRgYq9E6dO6693/kq79x80fZ9gDwAAAFZEsJcFE28eu7q61NLSEnNMU1OTuru75Xa75fF4ot7zer3q7e2NOW/yzWO2p9Z0uVxJHX/XXXfp7rvvzlBvssx4TjppskbD2aytBwAAgOzr7+/Xgw8+mOtuxOV0OuXxeHJWyRa+d0r3dKDh9dITFW+NwYltFHpIWDbLvGLvZAbW2PvXX++PG+pJ0vnlBHsAAACwHoK9LLHZbHr00UenXQTd5XKpsrJSbrc7av/GjRtNA8GJUr1JZc29JMRbX++swl3kHgAAAJipurrx6eoDgYD27dunCy64IKufn6lKQYfDkVSlJOJX7KV7jb0/vXpU39oTmPKY6vIFaf1MAAAAIBvMf6NGWrW0tGh0dHTaUC+sra1Ndrs9ap9hGGlfD6KiomLKbUzhJbNgr1RadFnWuwIAAADku/Lyct10002SpMHBwax//p49eyL9QG6VxVtjL80Vew/98vc6eip+WGibW6a3LKlI62cCAAAA2UDFXp564IEHtG7duqh9Xq83JvBLRapPrbrdbi1fvjzh4y29vt7+ndK+/5FKy6RLbpFefib2mIqLpNk88QkAAIDsW7t2rZxOZ8LHj46OJj21fqpuu+02fe9739PmzZt1xx13ZO1zA4GA/H6/SkpK0no/hZmJV7F3fIoQLllPHQjKu++luO8vsy1Q+3WXas4snnUGAACA9RDs5amGhgbZ7faoKr3JFXuT12dINahL9unV5cuX65prrknpMy3B/1/SL//+je39O82PO4v19QAAAJAbVVVVef8gndPpVE1NjZ555hndf//9+vznP5+Vz51YIVhdXZ2Vz0R8c8vMw7RjJ0+l7TMeHd1nuv/6889Sx3WXasFs/hQCAAAA6+LxtDxWU1Mz5fuVlZVR28musTf5+BUrViR1ftF49vHEjjt76v+9AAAAgGK3adMmhUIheTwebd++PeOfNzY2JrfbrZKS8ekfGxoaMv6ZmNq8WbNM9x9LU8XeC4eP6hcHgjH7F84u02evuZhQDwAAAJZHsJfHJk8TM3l7coXd3r17k2r/0KFDUds8vWoidEoynk3sWCr2AAAAgCk5HA595CMfUSgU0vr16zMe7rW3t0uSQqHx9duSma4UmRG3Yi9Nwd72Zw+Y7v/w8vNVMW9OWj4DAAAAyCWCvTw2uSJvcvBWV1cXtZ3sVJyTK/ZYb8LE0UPTHyNJZ1ZLc23THwcAAAAUue7ubtXU1ETCvUxNyXnPPfdocHBQJSUlKikpkdPp1AUXXJCRz0Li5sZZ1+7YydSDvZOnT+s/TYK9stISrbro3JTbBwAAAPIBwV4em1xRN3namMlB3549e5JqPxAIRG07HI6kzi8KR/6U2HFMwwkAAAAkrKenJ7JmeG9vr66//no98sgjaWl73759uummm9TX16eSkpJItV5bW1ta2kdq4lfspb7G3k/2H9TBoydi9teff5YqqdYDAABAgSDYy2OGYUReNzY2xrxvs9kiN8OS5Pf7k2p/4tSdTEkTR6LBHtNwAgAAAAmz2+363ve+pzPPPFOhUEh+v18dHR264oordP/99yf90KI0/qDjPffco/r6evl8PoVCIYVCIZWUlKixsVErV67MwE+CZM2Ns8be0TRU7O149o+m+xsvXpJy2wAAAEC+YNXoDPH7/Vq3bp22b98eFb4lY+fOnZHXd955p+kxK1eu1ODgYGTb5/MlXHk3MjISec0i8nEkHOxRsQcAAAAkw263a/v27WptbY0EeYZhyOPxyOPxSBp/ANHhcKiyslLl5eWRWUvGxsYUDAY1NjamoaEhDQ8PR5YaCFfohav1qqur9dBDD+XgJ4SZ2aUlKi2RToei96ejYs/3ohGz77yF87Ti3IqU2wYAAADyBcFehlRUVMjv98vtdqu7uzvp8/1+f6QCr7m5OW5Yt2bNmqhgz+v1Jhzs+Xy+yOvm5uak+1gUXksg2FuwRFqwOPN9AQAAAAqM3W7Xjh071NHRoW9961uR/eFwzuv1yuv1TttO+HhpPNAL73M4HHr00UfT3GukoqSkRHNnzdJrJ6ODvFTX2Dty4qSOnIgNB68//yyVvv6dAAAAAAoBU3FmiM1mU2Njo/r6+pKeIlOSOjs7JY3f6LpcrrjHNTU1RW339vYm1P7AwEDkdWNj44yrCgvekRenP4ZpOAEAAICUdHd3a/v27Vq5cmVUxV1YeFpNs3/hY8P/wvtbWlq0fft2lZeX5+RnQnxm6+wdPZVasPfya8dN9y9eMDeldgEAAIB8Q7CXQS0tLZLeCOkS5fF45PV6ZbPZEprKc2Lw5/f7NTQ0NO1nbN682fR8TJLIVJxnMw0nAAAAkCqHw6H+/n7t2rVLH/nIR1ReXh4V3knRAd7EyryJ/5qbm7Vr1y498MADufpRMI15s2L/FHE8xWDvYJxg76z5c1JqFwAAAMg3TMWZQQ0NDbLZbPJ6vXK73QkFaG63Wx6PR3a7XY888khClXRtbW3q7e2NVAbec889UwaCvb29kWk4u7q6ZLfbk/ipikgolNhUnFTsAQAAAGlTXV2t7u5udXd3KxAIyOv1amRkRIFAILKuXjAYVEVFhaqrq1VeXq4VK1bI6XSqpoaH7qxgbtmsmH1HT6a2xl68ij2CPQAAABQagr0Ma2pqUl9fnzwej/r6+tTc3Cyn0xkJ08bGxiJVdgMDAzIMQ83NzUmvy7d9+3Zdd911MgxDfr9fq1evVk9PT8x6ex6PR263W9J4IBiuKoSJE4elk69NfczcSmlhVXb6AwAAABSZ6upqNTc3syZ4gZlrUrF3LENTcS4i2AMAAECBIdjLsNtuu019fX2SJMMw5PF45PF4TI91Op1yuVwxYVwibDabdu/erfb2dg0ODsrv92vVqlWy2+2qqanR2NiYRkZGZBiGJKmnpydmfT5Mksg0nBf9mcRC7AAAwOI6OjoiazBXVFRIkoLBoKTx32G7urpSeiCst7c3Mj19eFaJiZ8T/h3V6XSqv79/xp8DwBpMg70UK/biTcVJsAcAAIBCQ7CXYQ6HQ6Ojo+rr69PQ0FBk+hjDMGS321VdXa2GhgY1NjamPCWmzWbT1q1b5fP5tG3bNu3cuVPBYFCDg4Oy2+2qra1VU1MTVXqJOvLH+O/NKZfs75Eu/kD2+gMAAJAht912m2pra+X3+yMPiU20cePGlH6HdDqd6urqkiSNjIyor68vEubZ7XZt2LBB5eXlqqurm/kPAcAyzKbizETF3hmzZ2meyWcBAAAAVkawlwU2m01tbW1qa2vLyuc5HI6kp/KEiddeNN//rn+UylmXEAAAFA6HwxGZNcLlckVN3y6NV+0luma0GbvdHvUQW3jdNElTrg0NoDCZVewdPZlasGdWscf6egAAAChEsb9NAxgXbyrO+Yuz2w8AAIAsa2trU2NjY9Q+j8cTU8mXKqfTSagHFKF5ZWZr7KU2FadZxR7TcAIAAKAQEewB8ZgFe7MXSrMXZL8vAAAAWdbQ0BCzb2IVXyoCgYAkzWhtaQDWN3eWyVScGanYm5tSmwAAAEA+ItgDzJw4Iv3hx7H7F5yT/b4AAADkgFl13uDgoIaGhtLWttPpTLktANYz17Ri77RCodCM2nvtxCkdORlb8cdUnAAAAChEBHvAZHt3SIO3mr9HsAcAAIqEz+eTJDU3N0ft37hxY0rtTgwM6+rqUmoLgDWZVexJ0vFTM6vaM5uGUyLYAwAAQGEi2AMmeu0l6enNUijO+g7zCfYAAEBxGBkZkSS5XK6oKTN9Pp96e3tn3G44MJTE+npAkTJbY08ar9qbiZdfO2a6nzX2AAAAUIgI9oCJDvxcCk1xM0nFHgAAKAKGYcgwDNlsNtlsNm3atCnq/VSq9lhfD8CcWeZ/ijg6w3X2zNbXk6jYAwAAQGEi2AMmeiUw9fvnXp2dfgAAAOTQ8PCwJKm2tlbSeAjX2NgYed8wDLnd7hm1/fTTT0e1DaD4xK/YizNzyjTiTcVJxR4AAAAKEcEeMNEr++K/d/ltUnl19voCAACQI3v27JEUXVXncrmijvF4PFHr5SUqXLG3bNmymXcQgKXFW2Pv2Ewr9o4S7AEAAKB4EOwBE8Wr2FvdK122Lrt9AQAAyJFwVZ3dbo/ss9vtamtrizpuJlV74TX2ampqZt5BAJY2N07F3tGZVuwdiQ32Fsyepfll5gEiAAAAYGVlue4AkDdOvia99mLs/jfdJM1blP3+AAAA5Ei4Yq+uri5qv8vlUl9fnwzDkCQNDg7K5/MlvF5e+DyztgHMzO233645c2Ir09avX6/W1tYc9Gh687JQscf6egAAAMgXPT092rp1a8z+48fNZ56YDsEeEPbK8+b7z7wgu/0AAADIsfAUm2aB3b333qvOzs7Idnt7u3bs2JFQu+G1+yTJZrOl2EsAknTw4EHT/YcPH85yTxIXr2Lv2KmZBXtma+wR7AEAACBfHD58WAcOHEhbewR7QFi89fUI9gAAsJZf/oM0lvzab3mr3C5d+RdZ+7jwVJnxgreWlpao9fV8Pp8GBgbU1NQ0bdvh9fUSrfADML1FixaZVuwtXLgwB71JzNxZcYK9k8lNxWkcPaFHf7VP+8Zei3mPYA8AAAD5YuHChVqyZEnM/uPHj8d9UG8qBHtAWNxgrzq7/QAAAKkZ80uHfp3rXlhWuKqutrY27jEPPPCA1q17Y/3hjo6OhIK9kZGRadsGkJyHH35Y11xzTa67kZR5cda+S6Zi78Sp0/rs94f1/CuxoZ4kLSLYAwAAQJ5obW01nSb/ySef1M0335x0e+aPyQHFyCzYK5svzTsr+30BAADIkXD4NlVVXUNDg5xOZ2TbMAy53e5p2w5X7C1btiy1TgKwtDlxKvaOJrHG3k/2H4wb6knSWfPnJt0vAAAAwAoI9oCwVwKx+868QCopyX5fAAAAciQc7NXV1U15XFdXV9S2x+ORYRgJtV1TU5NUn/x+v9avX6/169cndd50fD6fVq1apfr6+sgUpAAyL+5UnKcSn4rz1y+/MuX71eXzk+oTAAAAYBUEe4AknT4hvfqH2P0LWV8PAAAUl3BVnd1un/I4u92u5ubmqH3t7e1xjzcMIxL8TRcahvl8Pq1du1b19fUaHBzU2NhYQuclqr29XT6fT36/33RaFACZEXcqziQq9vYar8Z97/wz5+stSyqS7RYAAABgCayxB0jS4RekkMlN5JkEewAAWE751IGU5WTx55kYvk01FWeYy+XSwMBA5JzBwUH5fD7Tc8OBoSTZbLYp+9DX16fe3l75/f5kf4SkTAwKg8FgRj8LwBviV+wlHuz5jSOm+9dccp5aHNUqK+U5ZgAAABQmgj1AMp+GUyLYAwDAiq78i1z3wLKGh4clTV+tF2az2bRhw4ao9fXa29u1Y8eOuG1PFRh2dHSor68v0ofwdJ+dnZ2J/QBJ6unpUWtrq4LBoB566KGMfAaAWGWlJSotkU6HovcfPZnYVJyvnjipP756LGZ/08VLdOdbL05HFwEAAIC8xSNsgCQZvzffT7AHAACKyJ49eyQltwZeW1tbVBDo8/k0MDAQc1y4+q66ujpuW4FAQA6HQ4888oh27dqllpaWKY9PlcPh0K5duzQ6OqqGhoaMfQ6AaCUlJZo3K3Y6zuMJVuzFq9ZbVnFGSv0CAAAArIBgDzh1XNob+1S5SsukM87Lfn8AAABy5Omnn5aUeMVe2AMPPBC13dHREXOMz+eTJK1YsSJuO/39/dqxYwchG1AE5pbF/jniaILB3t5gnGDPtiClPgEAAABWQLAH+L8vHTsUu//sWqnUfFF3AACAQhSu2Kurq0vqvIaGBjmdzsi2YRjyeDxRx4TX2EumGhBA4ZprUrF3LMGpOP3Gq6b77QR7AAAAKAIEeyhup09K//sd8/cuvjm7fQEAAMix8HSZU62DF094Pbwwt9stwzBi2k62GhBAYZpnUrF3LNGKPZOpOCvnzVbFvDkp9wsAAADIdwR7KG77fiQd+VPs/opLpHOuzHZvAAAAciY8VaY0s/DNbrerubk5al97e7ukN0K9mbadSYZhqLe3N6bCcLKhoSF1dHREhZUej0f19fWqqqpSfX291q5dO207AMbNmWUyFedJ82Bv9KUxbfP59dAvn9NDv3xOvzwQjDlmmY319QAAAFAcynLdASCn9v2P+f433yqVlGS3LwAAADk0PDwsKbXgzeVyqa+vL7I9ODgon8+XUiVgJvT29mpkZEQjIyORQNPpdKqtrS1yjM/nk9fr1dNPP62dO3dGAj2XyyXDMHTrrbdGhaF+v19+v19er1e9vb3avn27bDZbdn8wwELM1tg7dip2Ks7H//cFffVnv5u2vWUVTMMJAACA4kDFHorbmD9235l26by3Zb8vAAAAOTQyMiIptTXwbDabXC5X1L729vZIaFhdXT3zDqaJ3+/XwMBAVKhn5rHHHtPQ0FBUqCeNB6CrV6+WND796I4dO9TT0xO1xqDf749UKwIwN890jb3oir2AcURbfv5sQu0tY309AAAAFAkq9lC8Th2Xjh2K3b/YIZWQeQMAgOISDvZSnSqzra1Nvb29kSo9n8+nQCAgSVqxYkVKbaeD3W5Xf3+/pPGpNN1ut+lx4YDSMAwtX748sn/dunXq6upSS0tLZJ/D4VBTU5PcbndkKs7BwUH5/f68m3oUyBfmFXtvBHuhUEhbfvGsToVCCbXHVJwAAAAoFqQXKF6vvWi+f/452e0HAABAHghXr1VWVqbc1r333hu1Ha54S6UaMBMSqSC02WxRU2q2tbVFhXoTuVyuqCBvqopAoNjNNavYmzAV59C+l0zX0ovHzlScAAAAKBIEeyheR+IEewsI9gAAQHHp7e2NvN67d2/K7TU1NZmup5dv1Wvl5eVJnxMv1AtbuXJl5HV4ClIAseaZVey9PhXn8VOn1fPL3yfc1tKF83TGbCYkAgAAQHHgN18UryN/Mt9PsAcAAIqAz+fT8PCwRkZG1NfXF9nf19enQCCghoYGlZeXq66uzjSkm86mTZu0atWqqH35FuwlqqKiIlJ1WFFRMeWxtbW1kf9/TlybD0C0ubPMp+I8HQrpp/sP6sUjx2Ler5g3W2+vPlv/8dsXova/79KlGesnAAAAkG8I9lC8XiPYAwAAxau9vV0+ny9qqsmKigoFg0GNjIzI6/VKGp96cibBnsPhUGNjowYHByPbxWC64A/AuLllsVNxStKRE6e0e//Lpu996soL9c5l5+japWfpX3/9vE6cOq132Bfrzy45L5NdBQAAAPIKwR6Kl1nFXmmZNLci610BAADIth07dmT8MzZt2hQJ9hJZzw5A8TCr2JOkm/91t+n+cxbM1Q32xZKkty6t1FuXpr4eKAAAAGBFBHsoXmbB3vxzpBKWngQAAEgHm82m/fv357obAPJQvGAvnrdVLVJJSUmGegMAAABYBwkGipfZVJxMwwkAAAAAGRdvKs543lZ1VoZ6AgAAAFgLwR6KU+i0dOSl2P0EewAAAACQcfOSqNibV1aqunNtGewNAAAAYB0EeyhORw9KoZOx++cvzn5fAAAAkFF+v19+vz/X3QAwwdyyxP8ccdWSSs1JcupOAAAAoFDxmzGK05EXzfdTsQcAAFAQDMOQ2+3WqlWrVF9fry1btsy4rWAwaPoawMzNnZX4VJxvq1qUwZ4AAAAA1lKW6w4AOWG2vp5EsAcAAJBnAoGA6evpeL1eeTyeyPZtt90Wc8zY2FjkdSYCO0JAIL55SVTsXbuUYA8AAAAIo2IPxekIwR4AAEC+MgxDPp9PAwMDUeGc3++X2+2Wz+eT3++XYRhx2xgeHo68djqdcjgcMe339vZG9vl8Pg0NDUWm7DQMQ36/XwMDA1Gfs2XLlmk/O2znzp1RbQJ4Q6IVe9eff5Yq58/JcG8AAAAA66BiD8XJNNgrkeafnfWuAAAA4A0dHR3q6+uL+77H44mEfTabTaOjo6bHeb3eyGuXyxV5PTQ0pHXr1pmeE97f2NioQCAgn88Xc0xfX1+kf/v375/yZzEMI9JmV1eXWlpapjwehckwDG3evFl9fX3avXu3bDZbrruUFxJZY2/52eXacPVFWegNAAAAYB0Ee5ix22+/XXPmxD45uX79erW2tuagRwkKhaSXTf4ANG+RVDo7+/0BAABARHd3t7q7u1NqI1yRJ0nNzc1R1XoNDQ3TBnKpaGpqymj7yerp6dHWrVtj9h8/fjwHvSkufr9fW7ZsiQqqg8Egwd7r5s6KH+z9/bvrtHjBHJ1zxrws9ggAAACwBoI9zNjBgwdN9x8+fDjLPUnCS89IP++WXnsx9j2m4QQAACgImzdvljRe0ZdqSGh1hw8f1oEDB3LdjaLi8/n0ta99TYODg7nuSl6bWxZ/Ks7LzjpTs0pLstgbAAAAwDoI9jBjixYtMq3YW7hwYQ56k4BTx6WffFk68ar5+wR7AAAABSFcIfXQQw/luCe5t3DhQi1ZsiRm//Hjx+M+qIfkGYahvr4+9fb2sqZiguZNUbFHqAcAAADER7CHGXv44Yd1zTXX5LobiXtpT/xQT5LmE+wBAABYndvtlmEYamtrU0NDQ667k3Otra2m0+Q/+eSTuvnmm3PQo8JiGIZaW1uj1nSUxqeA/fSnP63Vq1fLMIwc9S6/zZ89S4vmzdbBoyei9n+iblluOgQAAABYBMEeisfRl6Z+f3FddvoBAACAjDAMQx6PRw6HQy6XK9fdQRGw2WyRUM9ms2nDhg1qbm6OrKNXUVFBsBdHaUmJVl+0RH3P7Ivsq5g7W2suOS+HvQIAAADyH8EeisfRYPz3qpzSOQR7AAAAVhYMBtXT06OmpqZcdwVFpLGxUQ0NDWppaYl5r7y8PAc9so6P1dpVOX+OnjoQ1MI5ZVp3xQU6Yw5/pgAAAACmwm/MKB7Hgub7Vz4gnV0jlcRf4wEAAAD5z263y26357obKDJbt27NdRcsq6SkRO+7dKned+nSXHcFAAAAsAyCPRQPs2Bv9hnS4tqsdwUAAAAAAAAAACBZlCiheJgFe3Mrst0LAAAAAAAAAACAGSHYQ/E4ZrJoPcEeAAAAAAAAAACwCII9FA8q9gAAAAAAAAAAgIUR7KE4hE5TsQcAAAAAAAAAACyNYA/F4fgrkk7H7p9ry3pXAAAAAAAAAAAAZoJgD8XBbBpOSZpbmdVuAAAAAAAAAAAAzFRZrjsAZEXcYI+KPQAAAADWNTo6mtTxVVVVqqqqylBvAAAAgOK1f/9+7d+/P+Hjk/1dPoxgD8WBij0AAAAABcjlciV1/F133aW77747Q70BAAAAild/f78efPDBjH8OU3GiOFCxBwAAAAAAAAAALI5gD8XhaNB8/9yKbPYCAAAAAAAAAABgxpiKE8XBrGKvtEyafUbWuwIAAAAA6eJ2u7V8+fKEj2d9PQAAACAz1q5dK6fTmfDxo6OjSU+tLxHsoVgcM2L3za2QSkqy3hUAAAAASJfly5frmmuuyXU3AAAAgKJXVVWVlQfpmIoTxeHYodh9TMMJAAAAAAAAAAAshIo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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"LJ-mixing\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = population1, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12, data_label = r'$N_\\mathrm{1,in}$')\n", + " myplt.add_plot(x = time, y = population2, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12, data_label = r'$N_\\mathrm{2,in}$')\n", + " myplt.complete_panel(ylabel = r'$N_\\mathrm{in}$', xlabel = r'$t$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 1501, 500), x_boundaries=(-100, 1600),\n", + " y_ticks=np.arange(0, 211, 50), y_boundaries=(-20, 220))\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = smoothed_time, y = smoothed_coordination*100, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color3, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$C_{1-2} ~ (\\times 100)$', xlabel = r'$t$',\n", + " xpad = 10, legend=False, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 1501, 500), x_boundaries=(-100, 1600),\n", + " y_ticks=np.arange(0, 6.1, 1), y_boundaries=(0, 6.2))\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorial1/figures/initial.ipynb b/docs/sphinx/source/tutorial1/figures/initial.ipynb new file mode 100644 index 000000000..feb00e41b --- /dev/null +++ b/docs/sphinx/source/tutorial1/figures/initial.ipynb @@ -0,0 +1,188 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "47169cf9", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6c432fa9", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data+\"initial.log\")\n", + "Step_EM = log.get(\"Step\", run_num = 0)\n", + "PotEng_EM = log.get(\"TotEng\", run_num = 0) # PotEn = TotEn the same if KinEn = 0\n", + "KinEng_EM = log.get(\"TotEng\", run_num = 0)*0 # KinEn = 0\n", + "# During MD\n", + "timestep = 0.005\n", + "Step_MD = log.get(\"Step\", run_num = 1)\n", + "Time_MD = Step_MD*timestep # time unit\n", + "PotEng_MD = log.get(\"PotEng\", run_num = 1)\n", + "KinEng_MD = log.get(\"KinEng\", run_num = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "734addcc", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"LJ-energy\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7.1), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2, n_line=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = Step_EM, y = PotEng_EM, type = \"plot\", linewidth_data = 5,\n", + " marker = \"-\", data_color = color2, markersize = 12, data_label = r'$p_\\mathrm{e}$')\n", + " myplt.complete_panel(ylabel = r'$U$', xlabel = None,\n", + " xpad = 10, legend=False, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 1001, 250), x_boundaries=(-100, 1100.5),\n", + " y_ticks=np.arange(-2, 1.01, 1), y_boundaries=(-2.6, 1.2))\n", + " # Panel b\n", + " myplt.add_panel()\n", + " #myplt.add_plot(x = Time_MD-Time_MD[0], y = PotEng_MD+KinEng_MD, type = \"plot\", linewidth_data = 3,\n", + " # marker = \"-\", data_color = np.array([0.2, 0.2, 0.2]), markersize = 12, data_label = r'$e_\\mathrm{total}$')\n", + " myplt.add_plot(x = Time_MD-Time_MD[0], y = PotEng_MD, type = \"plot\", linewidth_data = 5,\n", + " marker = \"-\", data_color = color2, markersize = 12, data_label = r'$p_\\mathrm{e}$')\n", + " myplt.complete_panel(ylabel = None, xlabel = None,\n", + " xpad = 10, legend=False, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 81, 20), x_boundaries=(-6, 83),\n", + " y_ticks=np.arange(-2, 1.01, 1), y_boundaries=(-2.6, 1.2))\n", + "\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = Step_EM, y = KinEng_EM, type = \"plot\", linewidth_data = 5,\n", + " marker = \"-\", data_color = color2, markersize = 12, data_label = r'$k_\\mathrm{e}$')\n", + " myplt.complete_panel(ylabel = r'$K$', xlabel = r'$\\mathrm{step}$',\n", + " xpad = 10, legend=False, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 1001, 250), x_boundaries=(-100, 1100.5),\n", + " y_ticks=np.arange(0, 2.01, 1), y_boundaries=(-0.6, 2.6))\n", + " # Panel b\n", + " myplt.add_panel()\n", + " #myplt.add_plot(x = Time_MD-Time_MD[0], y = PotEng_MD+KinEng_MD, type = \"plot\", linewidth_data = 3,\n", + " # marker = \"-\", data_color = np.array([0.2, 0.2, 0.2]), markersize = 12, data_label = r'$e_\\mathrm{total}$')\n", + " myplt.add_plot(x = Time_MD-Time_MD[0], y = KinEng_MD, type = \"plot\", linewidth_data = 5,\n", + " marker = \"-\", data_color = color2, markersize = 12, data_label = r'$k_\\mathrm{e}$')\n", + " myplt.complete_panel(ylabel = None, xlabel = r'$t$',\n", + " xpad = 10, legend=False, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 81, 20), x_boundaries=(-6, 83),\n", + " y_ticks=np.arange(0, 2.01, 1), y_boundaries=(-0.6, 2.6))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level1/lennard-jones-fluid/lennard-jones-dm.png b/docs/sphinx/source/tutorial1/figures/lennard-jones-dm.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level1/lennard-jones-fluid/lennard-jones-dm.png rename to docs/sphinx/source/tutorial1/figures/lennard-jones-dm.png diff --git a/docs/sphinx/source/tutorials/figures/level1/lennard-jones-fluid/lennard-jones-pyplot.ipynb 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The atoms of type 1 are + represented as small green spheres and the atoms of type 2 as large blue spheres. + :height: 220 + :align: right + :class: only-dark + +.. figure:: avatars/lennard-jones-fluid-avatar-light.webp + :alt: The binary mixture simulated during Tutorial 1. The atoms of type 1 are + represented as small green spheres and the atoms of type 2 as large blue spheres. + :height: 220 + :align: right + :class: only-light + +The objective of this tutorial is to perform simple MD simulations +using LAMMPS. The system consists of a Lennard-Jones fluid composed of +neutral particles with two different effective diameters, contained +within a cubic box with periodic boundary conditions. In this tutorial, basic MD simulations in +the microcanonical (NVE) and canonical (NVT) ensembles are performed, +and basic quantities are calculated, including the potential and kinetic +energies. \ No newline at end of file diff --git a/docs/sphinx/source/tutorial1/lennard-jones-fluid.rst b/docs/sphinx/source/tutorial1/lennard-jones-fluid.rst new file mode 100644 index 000000000..36c4622f4 --- /dev/null +++ b/docs/sphinx/source/tutorial1/lennard-jones-fluid.rst @@ -0,0 +1,15 @@ +.. _lennard-jones-label: + +Lennard-Jones fluid +******************* + +.. container:: hatnote + + The very basics of LAMMPS through a simple example + +.. include:: introduction.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: ../shared/cite.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst diff --git a/docs/sphinx/source/tutorial1/tutorial.rst b/docs/sphinx/source/tutorial1/tutorial.rst new file mode 100644 index 000000000..e9f843807 --- /dev/null +++ b/docs/sphinx/source/tutorial1/tutorial.rst @@ -0,0 +1,785 @@ +My first input +============== + +To run a simulation using LAMMPS, you need to write an input script +containing a series of commands for LAMMPS to execute, similar to Python +or Bash scripts. For clarity, the input scripts for this tutorial will +be divided into five categories, which will be filled out step by step. +To set up this tutorial using LAMMPS graphical user interface +(LAMMPS--GUI), select ``Start LAMMPS Tutorial 1`` +from the ``Tutorials`` menu and follow the instructions. This will +select (or create, if needed) a folder, place the initial input +file **initial.lmp** in it, and open the file in the LAMMPS--GUI Editor window: + +.. code-block:: lammps + + # PART A - ENERGY MINIMIZATION + # 1) Initialization + # 2) System definition + # 3) Settings + # 4) Visualization + # 5) Run + +.. admonition:: If you are not using LAMMPS-GUI + :class: gui + + All tutorials can be followed without using LAMMPS-GUI. To + do so, create a new folder and add a file named **initial.lmp** + inside it. Open the file in a text editor of your choice and + copy the previous lines into it. + +Everything that appears after a hash symbol (#) is a comment +and ignored by LAMMPS. These five categories are not required in every input script an do not +necessarily need to be in that exact order. For instance, the ``Settings`` +and the ``Visualization`` categories could be inverted, or +the ``Visualization`` category could be omitted. However, note that +LAMMPS reads input files from top to bottom and processes each command +*immediately*. Therefore, the ``Initialization`` and +``System definition`` categories must appear at the top of the +input, and the ``Run`` category must appear at the bottom. Also, the +specifics of some commands can change after global settings are modified, so the +order of commands in the input script is important. + +Initialization +-------------- + +In the first section of the script, called ``Initialization``, +global parameters for the simulation are defined, such as units, boundary conditions +(e.g., periodic or non-periodic), and atom types (e.g., uncharged point particles +or extended spheres with a radius and angular velocities). These commands must be +executed *before* creating the simulation box or they will cause +an error. Similarly, many LAMMPS commands may only be +entered *after* the simulation box is defined. Only a limited +number of commands may be used in both cases. Update the **initial.lmp** file +so that the ``Initialization`` section appears as follows: + +.. code-block:: lammps + + # 1) Initialization + units lj + dimension 3 + atom_style atomic + boundary p p p + +The first line, ``units lj``, specifies the use of *reduced* +units, where all quantities are dimensionless. This unit system is a +popular choice for simulations that explore general statistical +mechanical principles, as it emphasizes relative differences between +parameters rather than representing any specific material. The second +line, ``dimension 3``, specifies that the simulation is conducted +in 3D space, as opposed to 2D, where atoms are confined to move only in +the xy-plane. The third line, ``atom_style atomic``, designates +the atomic style for representing simple, individual point particles. +In this style, each particle is treated as a point with a mass, making +it the most basic atom style. Other atom styles can incorporate +additional attributes for atoms, such as charges, bonds, or molecule +IDs, depending on the requirements of the simulated model. The last +line, ``boundary p p p``, indicates that periodic boundary +conditions are applied along all three directions of space, where the +three p stand for :math:`x`, :math:`y`, and :math:`z`, respectively. +Alternatives are fixed non-periodic (f), shrink-wrapped non-periodic (s), and +shrink-wrapped non-periodic with minimum (m). For non-periodic +boundaries, different options can be assigned to each dimension, making +configurations like ``boundary p p fm`` valid for systems such as +slab geometries. + +.. admonition:: Note + :class: non-title-info + + Strictly speaking, none of the four commands specified in the + ``Initialization`` section are mandatory, as they correspond to the + default settings for their respective global properties. However, + explicitly specifying these defaults is considered good practice to + avoid confusion when sharing input files with other LAMMPS users. + +Each LAMMPS command is accompanied by extensive online |lammpsdocs| +that details the different options for that command. +From the LAMMPS--GUI editor buffer, you can access the documentation by +right-clicking on a line containing a command (e.g., ``units lj``) +and selecting ``View Documentation for `units'``. This action +should prompt your web browser to open the corresponding URL for the +online manual. + +.. |lammpsdocs| raw:: html + + documentation + +The next step is to create the simulation box and populate it with +atoms. Modify the ``System definition`` category of +**initial.lmp** as shown below: + +.. code-block:: lammps + + # 2) System definition + region simbox block -20 20 -20 20 -20 20 + create_box 2 simbox + create_atoms 1 random 1500 34134 simbox overlap 0.3 + create_atoms 2 random 100 12756 simbox overlap 0.3 + +The first line, ``region simbox (...)``, defines a region named +``simbox`` that is a block (i.e., a rectangular cuboid) extending +from -20 to 20 units along all three spatial dimensions. The second +line, ``create_box 2 simbox``, initializes a simulation box based +on the region ``simbox`` and reserves space for two types of atoms. + +.. admonition:: Note + :class: non-title-info + + From this point on, any command referencing an atom type larger than 2 + will trigger an error. While it is possible to allocate more atom + types than needed, you must assign a mass and provide force field + parameters for each atom type. Failing to do so will cause LAMMPS to + terminate with an error. + +The third line, ``create_atoms (...)``, generates 1500 atoms of +type 1 at random positions within the ``simbox`` region. The +integer 34134 is a seed for the internal random number generator, which +can be changed to produce different sequences of random numbers and, +consequently, different initial atom positions. The fourth line adds +100 atoms of type 2. Both ``create_atoms`` commands use the +optional argument ``overlap 0.3``, which enforces a minimum +distance of 0.3 units between the randomly placed atoms. This +constraint helps avoid close contacts between atoms, which can lead +to excessively large forces and simulation instability. + +Settings +-------- + +Next, we specify the settings for the two atom types. Modify the +``Settings`` category of **initial.lmp** as follows: + +.. code-block:: lammps + + # 3) Settings + mass 1 1.0 + mass 2 5.0 + pair_style lj/cut 4.0 + pair_coeff 1 1 1.0 1.0 + pair_coeff 2 2 0.5 3.0 + +The two ``mass`` commands assign a mass of 1.0 and 5.0 units to the +atoms of type 1 and 2, respectively. The third line, +``pair_style lj/cut 4.0``, specifies that the atoms will be +interacting through a Lennard-Jones (LJ) potential with a cut-off equal +to :math:`r_c = 4.0` length units :cite:`wang2020lennard,fischer2023history`: + +.. math:: + :label: eq_LJ + + E_{ij}(r) = 4 \epsilon_{ij} \left[ \left( \dfrac{\sigma_{ij}}{r} \right)^{12} + - \left( \dfrac{\sigma_{ij}}{r} \right)^{6} \right], \quad \text{for} \quad r < r_c, + +where :math:`r` is the inter-particle distance, :math:`\epsilon_{ij}` is +the depth of the potential well that determines the interaction strength, and +:math:`\sigma_{ij}` is the distance at which the potential energy equals zero. +The indexes :math:`i` and :math:`j` refer to pairs of particle types. +The fourth line, ``pair_coeff 1 1 1.0 1.0``, specifies the +Lennard-Jones coefficients for interactions between pairs of atoms +of type 1: the energy parameter :math:`\epsilon_{11} = 1.0` and +the distance parameter :math:`\sigma_{11} = 1.0`. Similarly, the last line +sets the Lennard-Jones coefficients for interactions between atoms +of type 2, :math:`\epsilon_{22} = 0.5`, and :math:`\sigma_{22} = 3.0`. + +.. admonition:: Note + :class: non-title-info + + By default, LAMMPS calculates the cross coefficients for different atom + types using geometric average: :math:`\epsilon_{ij} = \sqrt{\epsilon_{ii} \epsilon_{jj}}`, + :math:`\sigma_{ij} = \sqrt{\sigma_{ii} \sigma_{jj}}`. In the present case, + :math:`\epsilon_{12} = \sqrt{1.0 \times 0.5} = 0.707`, and + :math:`\sigma_{12} = \sqrt{1.0 \times 3.0} = 1.732`. + +Single-point energy +------------------- + +The system is now fully parameterized, and the input is ready to compute +forces. Let us complete the two remaining categories, +``Visualization`` and ``Run``, by adding the following lines +to **initial.lmp**: + +.. code-block:: lammps + + # 4) Visualization + thermo 10 + thermo_style custom step etotal press + # 5) Run + run 0 post no + +The ``thermo 10`` command instructs LAMMPS to print thermodynamic +information to the console every specified number of steps, in this case, +every 10 simulation steps. The ``thermo_style custom`` command +defines the specific outputs, which in this case are the step number +(``step``), total energy :math:`E` (``etotal``), and pressure :math:`p` (``press``). +The ``run 0 post no`` command instructs LAMMPS to initialize forces and energy +without actually running the simulation. The ``post no`` option disables +the post-run summary and statistics output. + +You can now run LAMMPS (basic commands for running LAMMPS +are provided in :ref:`running-lammps-label`. +The simulation should finish quickly. + +With the default settings, LAMMPS--GUI will open two windows: one +displaying the console output and another with a chart. The ``Output`` window +will display information from the executed commands, including the +total energy and pressure at step 0, +as specified by the thermodynamic data request. Since no actual simulation +steps were performed, the ``Charts`` window will be empty. + +**Snapshot image --** At this point, you can create a snapshot image of the current system +using the ``Image Viewer`` window, which can be accessed by +clicking the ``Create Image`` button in the ``Run`` menu. The +image viewer works by instructing LAMMPS to render an image of the +current system using its internal rendering library via the ``dump image`` +command. The resulting image is then displayed, with various +buttons available to adjust the view and rendering style. This will always +capture the current state of the system. + +Energy minimization +------------------- + +Now, replace the ``run 0 post no`` command line with the +following ``minimize`` command: + +.. code-block:: lammps + + # 5) Run + minimize 1.0e-6 1.0e-6 1000 10000 + +This tells LAMMPS to perform an energy minimization of the system. +Specifically, LAMMPS will compute the forces on all atoms and then update their +positions according to a selected algorithm, aiming to reduce +the potential energy. By default, LAMMPS uses the conjugate gradient (CG) +algorithm :cite:`hestenes1952methods`. The simulation will stop as soon +as the minimizer algorithm cannot find a way to lower the potential +energy. Note that, except for trivial systems, minimization algorithms will find a +local minimum rather than the global minimum. + +Run the minimization and observe that LAMMPS-GUI captures the output +and updates the chart in real time. This run executes quickly (depending +on your computer's capabilities), but LAMMPS-GUI may fail to capture some +of the thermodynamic data. In that +case, use the ``Preferences`` dialog to reduce the data update +interval and switch to single-threaded, unaccelerated execution in the +``Accelerators`` tab. You can repeat the run; each new attempt will start +fresh, resetting the system and re-executing the script from the beginning. + +Run the minimization. The potential energy, :math:`U`, decreases +from a positive value to a negative value +(as can also be seen in the figure below). Note that +during energy minimization, the potential energy equals the total energy +of the system, :math:`E = U`, since the kinetic energy, :math:`K`, is zero. The +initially positive potential energy is expected, as the atoms are +created at random positions within the simulation box, with some in very +close proximity to each other. This proximity results in a large +initial potential energy due to the repulsive branch of the +Lennard-Jones potential [i.e., the term in :math:`1/r^{12}` in +Eq. :eq:`eq_LJ`]. As the energy minimization progresses, the energy +decreases - first rapidly - then more gradually, before plateauing at a +negative value. This indicates that the atoms have moved to reasonable +distances from one another. + +Create and save a snapshot image of the simulation state after the +minimization, and compare it to the initial image. You should observe +that the atoms are *clumping together* as they move toward positions +of lower potential energy. + +Molecular dynamics +------------------ + +After energy minimization, any overlapping atoms are displaced, and +the system is ready for a molecular dynamics simulation. To continue +from the result of the minimization step, append the MD simulation +commands to the same input script, **initial.lmp**. Add the +following lines immediately after the ``minimize`` command: + +.. code-block:: lammps + + # PART B - MOLECULAR DYNAMICS + # 4) Visualization + thermo 50 + thermo_style custom step temp etotal pe ke press + +Since LAMMPS reads inputs from top to bottom, these lines will +be executed *after* the energy minimization. Therefore, +there is no need to re-initialize or re-define the +system. The ``thermo`` command is called a second time to +update the output frequency from 10 to 50 as soon as ``PART B`` of +the simulation starts. In addition, a new ``thermo_style`` +command is introduced to specify the thermodynamic information LAMMPS should +print during ``PART B``. This adjustment is made because, during +molecular dynamics, the system exhibits a non-zero temperature :math:`T` (and +consequently a non-zero kinetic energy :math:`K`, keyword ``ke``), which are useful to monitor. +The ``pe`` keyword represents the potential energy of the system, :math:`E`, such that +:math:`U + K = E`. + +Then, add a second ``Run`` category by including the following +lines in ``PART B`` of **initial.lmp**: + +.. code-block:: lammps + + # 5) Run + fix mynve all nve + timestep 0.005 + run 50000 + +The ``fix nve`` command updates the positions and velocities of the +atoms in the group ``all`` at every step. The group ``all`` +is a default group that contains all atoms. The last two lines specify +the value of the ``timestep`` and the number of steps for the +``run``, respectively, for a total duration of 250 time units. + +.. admonition:: Note + :class: non-title-info + + Since no other fix commands alter forces or velocities, and periodic + boundary conditions are applied in all directions, the MD simulation + will be performed in the microcanonical (NVE) ensemble, which + maintains a constant number of particles and a fixed box volume. In + this ensemble, the system does not exchange energy with anything + outside the simulation box. + +Run the simulation using LAMMPS. Initially, there is no equilibrium +between potential and kinetic energy, as the potential energy +decreases while the kinetic energy increases. After approximately +40000 steps, the values for both kinetic and potential energy +plateau, indicating that the system has reached equilibrium, with +the total energy fluctuating around a certain constant value. + +Now, we change the ``Run`` section to (note the smaller number of +MD steps): + +.. code-block:: lammps + + # 5) Run + fix mynve all nve + fix mylgv all langevin 1.0 1.0 0.1 10917 + timestep 0.005 + run 15000 + +The new command adds a Langevin thermostat to the atoms in the group +``all``, with a target temperature of 1.0 temperature units +throughout the run (the two numbers represent the target temperature at +the beginning and at the end of the run, which results in a temperature +ramp if they differ) :cite:`schneider1978molecular`. A ``damping`` +parameter of 0.1 is used. It determines how rapidly the temperature is +relaxed to its desired value. In a Langevin thermostat, the atoms are +subject to friction and random noise (in the form of randomly added +velocities). Since a constant friction term removes more kinetic energy +from fast atoms and less from slow atoms, the system will eventually +reach a dynamic equilibrium where the kinetic energy removed and added +are about the same. The number 10917 is a seed used to initialize the +random number generator used inside of ``fix langevin``; you can +change it to perform statistically independent simulations. In the +presence of a thermostat, the MD simulation will be performed in the +canonical or NVT ensemble. + +Run the simulation again using LAMMPS. From the information +printed in the log file, one can see that the temperature +starts from 0 but rapidly reaches the requested value and +stabilizes itself near :math:`T=1` temperature units. One can also observe that +the potential energy, :math:`U`, rapidly decreases during energy +minimization (see the figure below). After +the molecular dynamics simulation starts, :math:`U` increases until +it reaches a plateau value of about -0.25. The kinetic energy, +:math:`K`, is equal to zero during energy minimization and then +increases rapidly during molecular dynamics until it reaches +a plateau value of about 1.5. + +From the information +printed in the ``Output`` window, one can see that the temperature +starts from 0 but rapidly reaches the requested value and +stabilizes itself near :math:`T=1` temperature units. One can also observe that +the potential energy, :math:`U`, rapidly decreases during energy +minimization (see the figure below). After +the molecular dynamics simulation starts, :math:`U` increases until +it reaches a plateau value of about -0.25. The kinetic energy, +:math:`K`, is equal to zero during energy minimization and then +increases rapidly during molecular dynamics until it reaches +a plateau value of about 1.5. + +.. figure:: figures/LJ-energy-dm.png + :class: only-dark + :alt: Evolution of the Lennard-Jones fluid energy + +.. figure:: figures/LJ-energy.png + :class: only-light + :alt: Evolution of the Lennard-Jones fluid energy + +.. container:: figurelegend + + Figure: (a) Potential energy, :math:`U`, of the binary mixture as a function of the + step during energy minimization. + (b) Potential energy, :math:`U`, as a function of time, :math:`t`, during molecular dynamics in + the NVT ensemble. (c) Kinetic energy, :math:`K`, during energy minimization. + (d) Kinetic energy, :math:`K`, during molecular dynamics. + +Trajectory visualization +------------------------ + +So far, the simulation has been mostly monitored through the analysis of +thermodynamic information. To better follow the evolution of the system +and visualize the trajectories of the atoms, let us print the positions +of the atoms in a file at a regular interval. + +Add the following command to the ``Visualization`` section +of ``PART B`` of the **initial.lmp** file: + +.. code-block:: lammps + + dump mydmp all atom 100 dump.lammpstrj + +Run the **initial.lmp** file using LAMMPS again. A file named **dump.lammpstrj** +must appear alongside **initial.lmp**. The **.lammpstrj** file can be opened +using VMD :cite:`humphrey1996vmd` or OVITO :cite:`stukowski2009visualization`. + +Use the ``dump image`` command to create snapshot images during the simulation. We +have already explored the ``Image Viewer`` window. Open it again +and adjust the visualization to your liking using the available buttons. +Now you can copy the commands used to create this visualization to the +clipboard by either using the ``Ctrl-D`` keyboard shortcut or +selecting ``Copy dump image command`` from the ``File`` menu. +This text can be pasted into the ``Visualization`` section +of ``PART B`` of the **initial.lmp** file. This may look like +the following: + +.. code-block:: lammps + + dump viz all image 100 myimage-*.ppm type type size 800 800 zoom 1.452 shiny 0.7 fsaa yes & + view 80 10 box yes 0.025 axes no 0.0 0.0 center s 0.483725 0.510373 0.510373 + dump_modify viz pad 9 boxcolor royalblue backcolor white adiam 1 1.6 adiam 2 4.8 + +This command tells LAMMPS to generate NetPBM format images every 100 +steps. The two ``type`` keywords are for *color* and +*diameter*, respectively. Run the **initial.lmp** using +LAMMPS again, and a new window named ``Slide Show`` will pop up. +It will show each image created by the ``dump image`` as it is +created. After the simulation is finished (or stopped), the slideshow +viewer allows you to animate the trajectory by cycling through the +images. The window also allows you to export the animation to a movie +(provided the FFMpeg program is installed) and to bulk delete those +image files. + +The rendering of the system can be further adjusted using the many +options of the ``dump image`` command. For instance, the value for the +``shiny`` keyword is used to adjust the shininess of the atoms, the +``box`` keyword adds or removes a representation of the box, and +the ``view`` and ``zoom`` keywords adjust the camera (and so on). + +Improving the script +==================== + +Let us improve the input script and perform more advanced operations, +such as specifying initial positions for the atoms and restarting the +simulation from a previously saved configuration. + +Control the initial atom positions +---------------------------------- + +Open the **improved.min.lmp**, which was downloaded during the +tutorial setup. This file contains the ``Part A`` of the +**initial.lmp** file, but *without* any +commands in the ``System definition`` section: + +.. code-block:: lammps + + # 1) Initialization + units lj + dimension 3 + atom_style atomic + boundary p p p + # 2) System definition + # 3) Settings + mass 1 1.0 + mass 2 10.0 + pair_style lj/cut 4.0 + pair_coeff 1 1 1.0 1.0 + pair_coeff 2 2 0.5 3.0 + # 4) Visualization + thermo 10 + thermo_style custom step etotal press + # 5) Run + minimize 1.0e-6 1.0e-6 1000 10000 + +We want to create the atoms of types 1 and 2 in two separate +regions. To achieve this, we need to add two ``region`` commands and then +reintroduce the ``create_atoms`` commands, this time using the new +regions instead of the simulation box region to place the atoms: + +.. code-block:: lammps + + # 2) System definition + region simbox block -20 20 -20 20 -20 20 + create_box 2 simbox + # for creating atoms + region cyl_in cylinder z 0 0 10 INF INF side in + region cyl_out cylinder z 0 0 10 INF INF side out + create_atoms 1 random 1000 34134 cyl_out + create_atoms 2 random 150 12756 cyl_in + +The ``side in`` and ``side out`` keywords are used to define +regions representing the inside and outside of the cylinder of radius +10 length units. Then, append a sixth section titled ``Save system`` at the end +of the file, ensuring that the ``write_data`` command is placed *after* +the ``minimize`` command: + +.. code-block:: lammps + + # 6) Save system + write_data improved.min.data + +.. admonition:: Note + :class: non-title-info + + A key improvement to the input is the addition of the + ``write_data`` command. This command writes the state of the + system to a text file called **improved.min.data**. This + **.data** file will be used later to restart the simulation from + the final state of the energy minimization step, eliminating the need + to repeat the system creation and minimization. + +Run the **improved.min.lmp** file using LAMMPS--GUI. At the end +of the simulation, a file called **improved.min.data** is created. + +You can view the contents of **improved.min.data** from LAMMPS--GUI, by +right-clicking on the file name in the editor and selecting the entry +``View file improved.min.data``. + +The created **.data** file contains all the information necessary +to restart the simulation, such as the number of atoms, the box size, +the masses, and the pair coefficients. This **.data** file also +contains the final positions of the atoms within the ``Atoms`` +section. The first five columns of the ``Atoms`` section +correspond (from left to right) to the atom indexes (from 1 to the total +number of atoms, 1150), the atom types (1 or 2 here), and the positions +of the atoms :math:`x`, :math:`y`, :math:`z`. The last three columns are image flags that +keep track of which atoms crossed the periodic boundary. The exact +format of each line in the ``Atoms`` section depends on the choice +of ``atom_style``, which determines which per-atom data is set and +stored internally in LAMMPS. + +.. admonition:: Note + :class: non-title-info + + Instead of the ``write_data`` command, you can also use the + ``write_restart`` command to save the state + of the simulation to a binary restart file. Binary restart files are + more compact, faster to write, and contain more information, making them often + more convenient to use. For example, the choice of ``atom_style`` + or ``pair_style`` is recorded, so those commands do not need to be issued + before reading the restart. Note however that restart files are not expected to be + portable across LAMMPS versions or platforms. Therefore, in these tutorials, + and with the exception of Tutorial 3, :ref:`all-atoms-label`, + we primarily use ``write_data`` to provide you with a reference + copy of the data file that works regardless of your LAMMPS version and platform. + +Restarting from a saved configuration +------------------------------------- + +To continue a simulation from the saved configuration, open the +**improved.md.lmp** file, which was downloaded during the tutorial setup. +This file contains the ``Initialization`` part from **initial.lmp** +and **improved.min.lmp**: + +.. code-block:: lammps + + # 1) Initialization + units lj + dimension 3 + atom_style atomic + boundary p p p + # 2) System definition + # 3) Settings + # 4) Visualization + # 5) Run + +Since we read most of the information from the data file, we don't need +to repeat all the commands from the ``System definition`` +and ``Settings`` categories. The exception is the ``pair_style`` +command, which now must come *before* the simulation box is defined, +meaning before the ``read_data`` command. Add the following +lines to **improved.md.lmp**: + +.. code-block:: lammps + + # 2) System definition + pair_style lj/cut 4.0 + read_data improved.min.data + +By visualizing the system, you may +have noticed that some atoms left their original region during +minimization. To start the simulation from a clean slate, with only +atoms of type 2 inside the cylinder and atoms of type 1 outside the +cylinder, let us delete the misplaced atoms by adding the following +commands to **improved.md.lmp**: + +.. code-block:: lammps + + region cyl_in cylinder z 0 0 10 INF INF side in + region cyl_out cylinder z 0 0 10 INF INF side out + group grp_t1 type 1 + group grp_t2 type 2 + group grp_in region cyl_in + group grp_out region cyl_out + group grp_t1_in intersect grp_t1 grp_in + group grp_t2_out intersect grp_t2 grp_out + delete_atoms group grp_t1_in + delete_atoms group grp_t2_out + +The first two ``region`` commands recreate the previously defined +regions, which is necessary since regions are not saved by the +``write_data`` command. The first two ``group`` commands +create groups containing all the atoms of type 1 and all the +atoms of type 2, respectively. The next two ``group`` commands +create atom groups based on their positions at the beginning of the +simulation, i.e., when the commands are being read by LAMMPS. The last +two ``group`` commands create atom groups based on the intersection +between the previously defined groups. Finally, the two +``delete_atoms`` commands delete the atoms of type 1 +located inside the cylinder and the atoms of type 2 located +outside the cylinder, respectively. + +Since LAMMPS has a limited number of custom groups (30), it is good practice +to delete groups that are no longer needed. This can be done by adding the +following four commands to **improved.md.lmp**: + +.. code-block:: lammps + + # delete no longer needed groups + group grp_in delete + group grp_out delete + group grp_t1_in delete + group grp_t2_out delete + +Let us monitor the number of atoms of each type inside the cylinder as a +function of time by creating the following equal-style variables: + +.. code-block:: lammps + + variable n1_in equal count(grp_t1,cyl_in) + variable n2_in equal count(grp_t2,cyl_in) + +The equal-style ``variables`` are expressions evaluated +during the run and return a number. Here, they are defined to count +the number of atoms of a specific group within the ``cyl_in`` region. + +In addition to counting the atoms in each region, we will also extract +the coordination number of type 2 atoms around type 1 atoms. The +coordination number measures the number of type 2 atoms near +type 1 atoms, defined by a cutoff distance. Taking the average provides +a good indicator of the degree of mixing in a binary mixture. This +is done using two ``compute`` commands: the first counts the +coordinated atoms, and the second calculates the average over all type 1 +atoms. Add the following lines to **improved.md.lmp**: + +.. code-block:: lammps + + compute coor12 grp_t1 coord/atom cutoff 2 group grp_t2 + compute sumcoor12 grp_t1 reduce ave c_coor12 + +The ``compute reduce ave`` command is used to average the per-atom +coordination number calculated by the ``coord/atom`` +compute command. Compute commands are not automatically invoked; they +require a *consumer* command that references the compute. In this case, the +first compute is referenced by the second, and we reference the second +in a ``thermo_style custom`` command (see below). + +.. admonition:: Note + :class: non-title-info + + There is no need for a ``Settings`` + section, as the settings are taken from the **.data** file. + +Finally, let us complete the script by adding the following lines to +**improved.md.lmp**: + +.. code-block:: lammps + + # 4) Visualization + thermo 1000 + thermo_style custom step temp pe ke etotal press v_n1_in v_n2_in c_sumcoor12 + dump viz all image 1000 myimage-*.ppm type type shiny 0.1 box no 0.01 view 0 0 zoom 1.8 fsaa yes size 800 800 + dump_modify viz adiam 1 1 adiam 2 3 acolor 1 turquoise acolor 2 royalblue backcolor white + +The two variables ``n1_in``, ``n2_in``, along with the compute +``sumcoor12``, were added to the list of information printed during +the simulation. Additionally, images of the system will be created with +slightly less saturated colors than the default ones. + +Finally, add the following lines to **improved.md.lmp**: + +.. code-block:: lammps + + # 5) Run + velocity all create 1.0 49284 mom yes dist gaussian + fix mynve all nve + fix mylgv all langevin 1.0 1.0 0.1 10917 zero yes + timestep 0.005 + run 300000 + +Here, there are a few more differences from the previous simulation. +First, the ``velocity create`` command assigns an initial velocity +to each atom. The initial velocity is chosen so that the average +initial temperature is equal to 1.0 temperature units. The additional +keywords ensure that no linear momentum (``mom yes``) is given to +the system and that the generated velocities are distributed according +to a Gaussian distribution. Another improvement is the ``zero +yes`` keyword in the Langevin thermostat, which ensures that the total +random force applied to the atoms is equal to zero. These steps are +important to prevent the system from starting to drift or move as a +whole. + +.. admonition:: Note + :class: non-title-info + + A bulk system with periodic boundary conditions is expected to remain + in place. Accordingly, when computing the temperature from the + kinetic energy, we use :math:`3N-3`` degrees of freedom since there is no + global translation. In a drifting system, some of the kinetic energy + is due to the drift, which means the system itself cools down. In + extreme cases, the system can freeze while its center of mass drifts + very quickly. This phenomenon is sometimes referred to as the + *flying ice cube syndrome* :cite:`wong2016good`. + +Run **improved.md.lmp** and observe the mixing of the two populations +over time. + +.. figure:: figures/mixing-vmd-dark.png + :class: only-dark + :alt: Evolution of the Lennard-Jones fluid mixing + +.. figure:: figures/mixing-vmd-light.png + :class: only-light + :alt: Evolution of the Lennard-Jones fluid mixing + +.. container:: figurelegend + + Figure: Evolution of the system during mixing. The + three snapshots show respectively the system at :math:`t = 0` (left panel), + :math:`t = 75` (middle panel), and :math:`t = 1500` (right panel). The atoms of type + 1 are represented as small green spheres and the atoms of type 2 as large cyan spheres. + +From the variables ``n1_in`` and ``n2_in``, you can track the number of atoms +in each region as a function of time (figure below, panel a). To view +their evolution, select the entries ``v_n1_in`` or ``v_n2_in`` in the ``Data`` +drop-down menu in the ``Charts`` window of LAMMPS--GUI. +In addition, as the mixing progresses, the average coordination number +between atoms of types 1 and 2 increases from about 0.01 to 0.04 +(figure below, panel b). This indicates that, over time, more and +more particles of type 1 come into contact with particles of type 2, as +expected during mixing. This can be observed using the entry +``c_sumcoor12`` in the ``Charts`` drop-down menu. + +.. figure:: figures/LJ-mixing-dm.png + :class: only-dark + :alt: Evolution of the Lennard-Jones fluid mixing + +.. figure:: figures/LJ-mixing.png + :class: only-light + :alt: Evolution of the Lennard-Jones fluid mixing + +.. container:: figurelegend + + Figure: a) Evolution of the numbers :math:`N_\text{1, in}$` and :math:`N_\text{2, in}` of atoms + of types 1 and 2, respectively, within the ``cyl_in`` region as functions + of time :math:`t`. b) Evolution of the coordination number :math:`C_{1-2}` + (compute ``sumcoor12``) between atoms of types 1 and 2. diff --git a/docs/avatars/level1/breaking-a-carbon-nanotube/CH.airebo b/docs/sphinx/source/tutorial2/avatars/CH.airebo similarity index 100% rename from docs/avatars/level1/breaking-a-carbon-nanotube/CH.airebo rename to docs/sphinx/source/tutorial2/avatars/CH.airebo diff --git a/docs/avatars/level1/breaking-a-carbon-nanotube/CNT-dark.png 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a/docs/sphinx/source/tutorial2/breaking-a-carbon-nanotube.rst b/docs/sphinx/source/tutorial2/breaking-a-carbon-nanotube.rst new file mode 100644 index 000000000..ccdb1916e --- /dev/null +++ b/docs/sphinx/source/tutorial2/breaking-a-carbon-nanotube.rst @@ -0,0 +1,16 @@ +.. _carbon-nanotube-label: + +Pulling on a carbon nanotube +**************************** + +.. container:: hatnote + + Stretching a carbon nanotube until it breaks + +.. include:: introduction.rst +.. include:: ../shared/recommend-tutorial1.rst +.. include:: ../shared/cite.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst diff --git a/docs/sphinx/source/tutorial2/exercises.rst b/docs/sphinx/source/tutorial2/exercises.rst new file mode 100644 index 000000000..efb26bcc2 --- /dev/null +++ b/docs/sphinx/source/tutorial2/exercises.rst @@ -0,0 +1,116 @@ + +Going further with exercises +============================ + +Plot the strain-stress curves +----------------------------- + +.. container:: justify + + Adapt the current scripts and extract the strain-stress curves for + the two breakable and unbreakable CNTs: + +.. figure:: figures/stress-strain-curve-dark.png + :alt: strain strain curve of the CNTs + :class: only-dark + +.. figure:: figures/stress-strain-curve-light.png + :alt: strain strain curve of the CNTs + :class: only-light + +.. container:: figurelegend + + Figure: Strain-stain curves for the two CNTs, breakable and unbreakable. + +Solve the flying ice cube artifact +---------------------------------- + +The flying ice cube effect is one of the most famous artifacts of +molecular simulations :cite:`wong2016good`. +Download this seemingly simple |input_flying_cube|, which is a simplified +version of the input from the first part of the tutorial. +Run the input with this |data_flying_cube| file +and this |parm_flying_cube| file. + +.. |input_flying_cube| raw:: html + + input + +.. |data_flying_cube| raw:: html + + data + +.. |parm_flying_cube| raw:: html + + parameter + +When you run this simulation using LAMMPS, you should see that the temperature is +very close to :math:`300\,\text{K}`, as expected. + +.. code-block:: bash + + Step Temp E_pair E_mol TotEng Press + 0 327.4142 589.20707 1980.6012 3242.2444 60.344754 + 1000 300.00184 588.90015 1980.9013 3185.9386 51.695282 + (...) + +However, if you look at the system using VMD, the atoms are not moving. + +Can you identify the origin of the issue, and fix the input? + +Insert gas in the carbon nanotube +--------------------------------- + +Modify the input from the unbreakable CNT, and add atoms of argon +within the CNT. + +Use the following *pair_coeff* for the argon, +and a mass of *39.948*: + +.. code-block:: lammps + + pair_coeff 2 2 0.232 3.3952 + +.. figure:: figures/CNT-gas-dark.png + :alt: CNT with Argon modeled in LAMMPS + :class: only-dark + +.. figure:: figures/CNT-gas-light.png + :alt: CNT with Argon modeled in LAMMPS + :class: only-light + +.. container:: figurelegend + + Figure: Argon atoms in a CNT. See the corresponding |gas_cnt_video|. + +.. |gas_cnt_video| raw:: html + + video + +Make a membrane of CNTs +----------------------- + +Replicate the CNT along the *x* +and *y* direction, and equilibrate the system to +create an infinite membrane made of multiple CNTs. + +Apply a shear deformation along *xy*. + +.. figure:: figures/membrane-dark.png + :alt: deformed membrane of CNTs + :class: only-dark + +.. figure:: figures/membrane-light.png + :alt: deformed membrane of CNTs + :class: only-light + +.. container:: figurelegend + + Figure: Multiple carbon nanotubes forming a membrane. + +.. admonition:: Hint + :class: info + + The box must be converted to triclinic to support deformation + along *xy*. + diff --git a/docs/sphinx/source/tutorial2/figures/CNT-breakable-stress-energy-dm.png b/docs/sphinx/source/tutorial2/figures/CNT-breakable-stress-energy-dm.png new file mode 100644 index 000000000..fdae78751 Binary files /dev/null and b/docs/sphinx/source/tutorial2/figures/CNT-breakable-stress-energy-dm.png differ diff --git a/docs/sphinx/source/tutorial2/figures/CNT-breakable-stress-energy.png b/docs/sphinx/source/tutorial2/figures/CNT-breakable-stress-energy.png new file mode 100644 index 000000000..8c5ca10fd Binary files /dev/null and b/docs/sphinx/source/tutorial2/figures/CNT-breakable-stress-energy.png differ diff --git a/docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/CNT-gas-dark.png b/docs/sphinx/source/tutorial2/figures/CNT-gas-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/CNT-gas-dark.png rename to docs/sphinx/source/tutorial2/figures/CNT-gas-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/CNT-gas-light.png b/docs/sphinx/source/tutorial2/figures/CNT-gas-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/CNT-gas-light.png rename to docs/sphinx/source/tutorial2/figures/CNT-gas-light.png diff --git a/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-length-energy-dm.png b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-length-energy-dm.png new file mode 100644 index 000000000..0fd15826d Binary files /dev/null and b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-length-energy-dm.png differ diff --git a/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-length-energy.png b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-length-energy.png new file mode 100644 index 000000000..693d0a809 Binary files /dev/null and b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-length-energy.png differ diff --git a/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-stress-strain-dm.png b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-stress-strain-dm.png new file mode 100644 index 000000000..d66a727f5 Binary files /dev/null and b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-stress-strain-dm.png differ diff --git a/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-stress-strain.png b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-stress-strain.png new file mode 100644 index 000000000..fd1c4bc0a Binary files /dev/null and b/docs/sphinx/source/tutorial2/figures/CNT-unbreakable-stress-strain.png differ diff --git a/docs/sphinx/source/tutorial2/figures/breakable.ipynb b/docs/sphinx/source/tutorial2/figures/breakable.ipynb new file mode 100644 index 000000000..840d26d9b --- /dev/null +++ b/docs/sphinx/source/tutorial2/figures/breakable.ipynb @@ -0,0 +1,218 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/simon/.local/lib/python3.12/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c0471c3", + "metadata": {}, + "outputs": [], + "source": [ + "def sliding_average(data, window_size):\n", + " \"\"\"Calculate the sliding (moving) average of a dataset with edge handling.\"\"\"\n", + " pad_width = window_size // 2\n", + " padded_data = np.pad(data, pad_width, mode='edge')\n", + " smoothed_data = np.convolve(padded_data, np.ones(window_size) / window_size, mode='valid')\n", + " return smoothed_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6156a6b", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"breakable.log\")\n", + "timestep = 1 # fs\n", + "time_0 = log.get(\"Step\", run_num=0)\n", + "TotEng_0 = log.get(\"TotEng\", run_num=0) # Kcal/mol\n", + "time_1 = log.get(\"Step\", run_num=1)\n", + "TotEng_1 = log.get(\"TotEng\", run_num=1) # Kcal/mol\n", + "\n", + "time_0 /= 1000 # ps\n", + "time_1 /= 1000 # ps\n", + "TotEng_0 /= 1000 # Mcal/mol\n", + "TotEng_1 /= 1000 # Mcal/mol\n", + "\n", + "stress_strain = np.loadtxt(path_data + \"breakable.dat\")\n", + "strain = sliding_average(stress_strain[:,0], 10)\n", + "stress = sliding_average(stress_strain[:,1], 10)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7f303b27", + "metadata": {}, + "outputs": [], + "source": [ + "#smoothed_time = sliding_average(time, 5)\n", + "#TotEng = sliding_average(TotEng, 5)\n", + "#Lcnt = sliding_average(Lcnt, 5)\n", + "#Fcnt = sliding_average(Fcnt, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "35962fed", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"CNT-breakable-stress-energy\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18, 7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time_0, y = TotEng_0, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.add_plot(x = time_1, y = TotEng_1, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " x = np.linspace(-5.2, -3.8)\n", + " myplt.add_plot(x = x*0+10, y = x, type = \"plot\", linewidth_data = 1.5,\n", + " marker = \"--\", data_color = color0, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$E ~ \\mathrm{(Mcal/mol)}$',\n", + " xlabel = r'$t~\\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 41, 10), y_ticks=np.arange(-5, -3.9, 0.2),\n", + " x_boundaries=(-1, 41), y_boundaries=(-5.1, -3.9))\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = stress_strain[:,0], y = stress_strain[:,1], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " myplt.add_plot(x = strain, y = stress, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$F_\\mathrm{cnt}/A_\\mathrm{cnt} ~ (\\mathrm{kcal/mol/\\AA}^3)$',\n", + " xlabel = r'$\\Delta L_\\mathrm{cnt} \\textrm{(\\%)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 41, 10), y_ticks=np.arange(-0.5, 2.1, 0.5),\n", + " x_boundaries=(-1, 41), y_boundaries=(-0.6, 2.1))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/colored-edge-def-dark.png b/docs/sphinx/source/tutorial2/figures/colored-edge-def-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/colored-edge-def-dark.png rename to docs/sphinx/source/tutorial2/figures/colored-edge-def-dark.png 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a/docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/stress-strain-unbreakable-pyplot.ipynb b/docs/sphinx/source/tutorial2/figures/stress-strain-unbreakable-pyplot.ipynb similarity index 100% rename from docs/sphinx/source/tutorials/figures/level1/breaking-a-carbon-nanotube/stress-strain-unbreakable-pyplot.ipynb rename to docs/sphinx/source/tutorial2/figures/stress-strain-unbreakable-pyplot.ipynb diff --git a/docs/sphinx/source/tutorial2/figures/unbreakable.ipynb b/docs/sphinx/source/tutorial2/figures/unbreakable.ipynb new file mode 100644 index 000000000..a217e84f0 --- /dev/null +++ b/docs/sphinx/source/tutorial2/figures/unbreakable.ipynb @@ -0,0 +1,284 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/simon/.local/lib/python3.12/site-packages/matplotlib/projections/__init__.py:63: UserWarning: Unable to import Axes3D. This may be due to multiple versions of Matplotlib being installed (e.g. as a system package and as a pip package). As a result, the 3D projection is not available.\n", + " warnings.warn(\"Unable to import Axes3D. This may be due to multiple versions of \"\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c0471c3", + "metadata": {}, + "outputs": [], + "source": [ + "def sliding_average(data, window_size):\n", + " \"\"\"Calculate the sliding (moving) average of a dataset with edge handling.\"\"\"\n", + " pad_width = window_size // 2\n", + " padded_data = np.pad(data, pad_width, mode='edge')\n", + " smoothed_data = np.convolve(padded_data, np.ones(window_size) / window_size, mode='valid')\n", + " return smoothed_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6156a6b", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"unbreakable.log\")\n", + "timestep = 1 # fs\n", + "time_0 = log.get(\"Step\", run_num=0)\n", + "TotEng_0 = log.get(\"TotEng\", run_num=0) # Kcal/mol\n", + "Lcnt_0 = log.get(\"v_Lcnt\", run_num=0) # Angstrom\n", + "time_1 = log.get(\"Step\", run_num=1)\n", + "TotEng_1 = log.get(\"TotEng\", run_num=1) # Kcal/mol\n", + "Lcnt_1 = log.get(\"v_Lcnt\", run_num=1) # Angstrom\n", + "time_0 /= 1000 # ps\n", + "time_1 /= 1000 # ps\n", + "TotEng_0 /= 1000 # Mcal/mol\n", + "TotEng_1 /= 1000 # Mcal/mol" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7f303b27", + "metadata": {}, + "outputs": [], + "source": [ + "#smoothed_time = sliding_average(time, 5)\n", + "#TotEng = sliding_average(TotEng, 5)\n", + "#Lcnt = sliding_average(Lcnt, 5)\n", + "#Fcnt = sliding_average(Fcnt, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d58b14c6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"CNT-unbreakable-length-energy\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time_0, y = Lcnt_0-Lcnt_0[0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.add_plot(x = time_1, y = Lcnt_1-Lcnt_0[0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " x = np.linspace(-0.5, 10.5)\n", + " myplt.add_plot(x = x*0+5, y = x, type = \"plot\", linewidth_data = 1.5,\n", + " marker = \"--\", data_color = color0, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$L_\\mathrm{cnt} - L_\\mathrm{cnt-0} \\mathrm{(\\AA)}$',\n", + " xlabel = r'$t~\\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 18, 5), y_ticks=np.arange(0, 11, 2),\n", + " x_boundaries=(-0.8, 16.2), y_boundaries=(-1, 11))\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time_0, y = TotEng_0, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.add_plot(x = time_1, y = TotEng_1, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " x = np.linspace(-1, 21)\n", + " myplt.add_plot(x = x*0+5, y = x, type = \"plot\", linewidth_data = 1.5,\n", + " marker = \"--\", data_color = color0, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$E ~ \\mathrm{(Mcal/mol)}$',\n", + " xlabel = r'$t~\\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 18, 5), y_ticks=np.arange(0, 25, 5),\n", + " x_boundaries=(-0.8, 16.2), y_boundaries=(-1, 21))\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c372356a", + "metadata": {}, + "outputs": [], + "source": [ + "stress_strain = np.loadtxt(path_data + \"unbreakable.dat\")\n", + "strain = sliding_average(stress_strain[:,0], 50)\n", + "stress = sliding_average(stress_strain[:,1], 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "11a67c94", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"CNT-unbreakable-stress-strain\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,6), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = stress_strain[:,0], y = -stress_strain[:,1], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " myplt.add_plot(x = strain, y = -stress, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$F_\\mathrm{cnt}/A_\\mathrm{cnt} ~ (\\mathrm{kcal/mol/\\AA}^3)$',\n", + " xlabel = r'$\\Delta L_\\mathrm{cnt} \\textrm{(\\%)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 20, 4), y_ticks=np.arange(0, 55, 10),\n", + " x_boundaries=(-0.8, 19.2), y_boundaries=(-1, 54))\n", + " # Print figure\n", + " # myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorial2/introduction.rst b/docs/sphinx/source/tutorial2/introduction.rst new file mode 100644 index 000000000..d340f2bdc --- /dev/null +++ b/docs/sphinx/source/tutorial2/introduction.rst @@ -0,0 +1,21 @@ +.. figure:: avatars/CNT_dark.webp + :alt: carbon nanotube image in vacuum + :height: 250 + :align: right + :class: only-dark + +.. figure:: avatars/CNT_light.webp + :alt: carbon nanotube image in vacuum + :height: 250 + :align: right + :class: only-light + +In this tutorial, the system of interest is a small, single-walled carbon +nanotube (CNT) in an empty box. The CNT is strained +by imposing a constant velocity on the edge atoms. To illustrate the +difference between conventional and reactive force fields, this tutorial +is divided into two parts: in the first part, a conventional molecular +force field (called OPLS-AA :cite:`jorgensenDevelopmentTestingOPLS1996`) +is used and the bonds between the atoms of the CNT are unbreakable. In +the second part, a reactive force field (called AIREBO :cite:`stuart2000reactive`) +is used, which allows chemical bonds to break under large strain. diff --git a/docs/sphinx/source/tutorial2/tutorial.rst b/docs/sphinx/source/tutorial2/tutorial.rst new file mode 100644 index 000000000..98fcb7449 --- /dev/null +++ b/docs/sphinx/source/tutorial2/tutorial.rst @@ -0,0 +1,600 @@ +Unbreakable bonds +================= + +With most conventional molecular force fields, the chemical bonds between +atoms are defined at the start of the simulation and remain fixed, regardless +of the forces applied to the atoms. These bonds are typically modeled as springs +with equilibrium distances :math:`r_0` and force constants :math:`k_\text{b}`: +:math:`U_\text{b} = k_\text{b} \left( r - r_0 \right)^2`. Additionally, angular and +dihedral constraints are often imposed to preserve the molecular structure +by maintaining the relative orientations of neighboring atoms. + +The LAMMPS input +---------------- + +To begin this tutorial, if you are using LAMMPS--GUI, select +``Start Tutorial 2`` from the ``Tutorials`` menu of LAMMPS-GUI +and follow the instructions. This will select a folder, create one if +necessary, and place several files into it. The initial input file, +set up for a single-point energy calculation, will also be loaded into +the editor under the name **unbreakable.lmp**. Additional files +are a data file containing the CNT topology and geometry, named +**unbreakable.data**, a parameters file named **unbreakable.inc**, as well as +the scripts required for the second part of the tutorial. + +.. code-block:: lammps + + units real + atom_style molecular + boundary f f f + + pair_style lj/cut 14.0 + bond_style harmonic + angle_style harmonic + dihedral_style opls + improper_style harmonic + special_bonds lj 0.0 0.0 0.5 + + read_data unbreakable.data + include unbreakable.inc + + run 0 post no + +.. admonition:: If you are not using LAMMPS-GUI + :class: gui + + Create a folder if needed and + place the initial input file, **unbreakable.lmp**, into it. Then, open the + file in a text editor of your choice, and copy the previous lines into it. + +The chosen unit system is ``real`` (therefore distances are in +Ångströms (Å), times in femtoseconds (fs), and energies in kcal/mol), the +``atom_style`` is ``molecular`` (therefore atoms are point +particles that can form bonds with each other), and the boundary +conditions are fixed. The boundary conditions do not matter here, as +the box boundaries were placed far from the CNT. Just like in the +previous tutorial, :ref:`lennard-jones-label`, +the pair style is ``lj/cut`` (i.e. a Lennard-Jones potential with +cutoff) and its cutoff is set to 14 Å, which means that only the +atoms closer than this distance interact through the Lennard-Jones +potential. + +The ``bond_style``, ``angle_style``, ``dihedral_style``, and ``improper_style`` +commands specify the different potentials used to constrain the relative +positions of the atoms. The ``special_bonds`` command sets the weighting factors +for the Lennard-Jones interactions between atoms directly connected by +one bond, two bonds, and three bonds, respectively. This is done for +convenience when parameterizing the force constants for bonds, angles, and +so on. By excluding the non-bonded (Lennard-Jones) interactions for +these pairs, those interactions do not need to be considered when determining +the force constants. + +The ``read_data`` command imports the |unbreakable_data| +file that should be downloaded next to **unbreakable.lmp**. This file contains information about the box size, atom positions, +as well as the identity of the atoms that are linked by ``bonds``, ``angles``, +``dihedrals``, and ``impropers`` interactions. It was created using VMD and TopoTools +:cite:`kohlmeyer2017topotools`. + +.. |unbreakable_data| raw:: html + + unbreakable.data + +.. admonition:: Note + :class: non-title-info + + The format details of the different sections in a data file change with different + settings. In particular, the ``Atoms`` section may have a different number of + columns, or the columns may represent different properties when the ``atom_style`` + is changed. To help users, LAMMPS and tools like VMD and TopoTools will add a + comment (here ``# molecular``) to the ``Atoms`` header line in the data files that + indicates the intended ``atom_style``. LAMMPS will print a warning when the chosen + atom style does not match what is written in that comment. + +The **.data** file does not contain any sections with potential parameters; thus, +we need to specify the parameters of both the bonded and +non-bonded potentials. The parameters we use are taken +from the OPLS-AA (Optimized Potentials for Liquid Simulations-All-Atom) +force field :cite:`jorgensenDevelopmentTestingOPLS1996`, and are given +in a separate **unbreakable.inc** file (also downloaded during +the tutorial setup). This file - that must be placed +next to **unbreakable.lmp** - contains the following lines: + +.. code-block:: lammps + + pair_coeff 1 1 0.066 3.4 + bond_coeff 1 469 1.4 + angle_coeff 1 63 120 + dihedral_coeff 1 0 7.25 0 0 + improper_coeff 1 5 180 + +The ``pair_coeff`` command sets the parameters for non-bonded +Lennard-Jones interactions atom type 1 to +:math:`\epsilon_{11} = 0.066 \, \text{kcal/mol}` and +:math:`\sigma_{11} = 3.4 \, \text{Å}`. The ``bond_coeff`` provides +the equilibrium distance :math:`r_0 = 1.4 \, \text{Å}` and the +spring constant :math:`k_\text{b} = 469 \, \text{kcal/mol/Å}^2` for the +harmonic potential imposed between two neighboring carbon atoms. The potential +is given by :math:`U_\text{b} = k_\text{b} ( r - r_0)^2`. The +``angle_coeff`` gives the equilibrium angle :math:`\theta_0` and +constant for the potential between three neighboring atoms : +:math:`U_\theta = k_\theta ( \theta - \theta_0)^2`. The +``dihedral_coeff`` and ``improper_coeff`` define the potentials +for the constraints between 4 atoms. + +.. admonition:: Note + :class: non-title-info + + Rather than copying the contents of the file into the input, we + incorporate it using the ``include`` command. Using ``include`` allows + us to conveniently reuse the parameter settings + in other inputs or switch them with others. This will become more general + when using type labels, which is shown in the next + tutorial :cite:`gissinger2024type`. + +Prepare the initial state +------------------------- + +In this tutorial, a deformation will be applied to the CNT by displacing +the atoms located at its edges. To achieve this, we will first isolate the +atoms at the two edges and place them into groups named ``rtop`` and +``rbot``. Add the following lines to **unbreakable.lmp**, +just before the ``run 0`` command: + +.. code-block:: lammps + + group carbon_atoms type 1 + variable xmax equal bound(carbon_atoms,xmax)-0.5 + variable xmin equal bound(carbon_atoms,xmin)+0.5 + region rtop block ${xmax} INF INF INF INF INF + region rbot block INF ${xmin} INF INF INF INF + region rmid block ${xmin} ${xmax} INF INF INF INF + +The first command includes all the atoms of type 1 (i.e. all the atoms here) +in a group named ``carbon_atoms``. +The variable :math:`x_\text{max}` corresponds to the coordinate of the +last atoms along :math:`x` minus :math:`0.5 \, \text{Å}`, and :math:`x_\text{min}` to the coordinate +of the first atoms along :math:`x` plus :math:`0.5 \, \text{Å}`. Then, three regions are defined, +corresponding to the following: :math:`x < x_\text{min}` (``rbot``, for region +bottom), :math:`x_\text{min} > x > x_\text{max}` (``rmid``, for region middle), +and :math:`x > x_\text{max}` (``rtop``, for region top). + +Finally, let us define 3 groups of atoms corresponding to the atoms +in each of the 3 regions by adding to **unbreakable.lmp** +just before the ``run 0`` command: + +.. code-block:: lammps + + group cnt_top region rtop + group cnt_bot region rbot + group cnt_mid region rmid + set group cnt_top mol 1 + set group cnt_bot mol 2 + set group cnt_mid mol 3 + +With the three ``set`` commands, we assign unique, otherwise unused +molecule IDs to atoms in those three groups. We will use this IDs later to +assign different colors to these groups of atoms. + +Run the simulation using LAMMPS. The number of atoms in each group is given in +the ``Output`` window. It is an important check to make sure that the number +of atoms in each group corresponds to what is expected, as shown here: + +.. code-block:: lammps + + 700 atoms in group carbon_atoms + 10 atoms in group cnt_top + 10 atoms in group cnt_bot + 680 atoms in group cnt_mid + +Finally, to start from a less ideal state and create a system with some defects, +let us randomly delete a small fraction of the carbon atoms. To avoid deleting +atoms that are too close to the edges, let us define a new region named ``rdel`` +that starts at :math:`2 \, \text{Å}` from the CNT edges: + +.. code-block:: lammps + + variable xmax_del equal ${xmax}-2 + variable xmin_del equal ${xmin}+2 + region rdel block ${xmin_del} ${xmax_del} INF INF INF INF + group rdel region rdel + delete_atoms random fraction 0.02 no rdel NULL 2793 bond yes + +The ``delete_atoms`` command randomly deletes :math:`2\,\%` of the atoms from +the ``rdel`` group, here about 10 atoms. + +The molecular dynamics +---------------------- + +Let us give an initial temperature to the atoms of the group ``cnt_mid`` +by adding the following commands to **unbreakable.lmp**: + +.. code-block:: lammps + + reset_atoms id sort yes + velocity cnt_mid create 300 48455 mom yes rot yes + +Re-setting the atom IDs is necessary before using the ``velocity`` command +when atoms were deleted, which is done here with the ``reset_atoms`` command. +The ``velocity`` command gives initial velocities to the atoms of the middle +group ``cnt_mid``, ensuring an initial temperature of :math:`T = 300\,\text{K}` +for these atoms. + +Let us specify the thermalization and the dynamics of the system. Add the following +lines into **unbreakable.lmp**: + +.. code-block:: lammps + + fix mynve1 cnt_top nve + fix mynve2 cnt_bot nve + fix mynvt cnt_mid nvt temp 300 300 100 + +The ``fix nve`` commands are applied to the atoms of ``cnt_top`` and +``cnt_bot``, respectively, and will ensure that the positions of the atoms +from these groups are recalculated at every step. The ``fix nvt`` does the +same for the ``cnt_mid`` group, while also applying a Nosé-Hoover thermostat +with desired temperature of 300 K :cite:`nose1984unified, hoover1985canonical`. +To restrain the motion of the atoms at the edges, let us add the following +commands to **unbreakable.lmp**: + +.. code-block:: lammps + + fix mysf1 cnt_top setforce 0 0 0 + fix mysf2 cnt_bot setforce 0 0 0 + velocity cnt_top set 0 0 0 + velocity cnt_bot set 0 0 0 + +The two ``setforce`` commands cancel the forces applied on the atoms of the +two edges, respectively. The cancellation of the forces is done at every step, +and along all 3 directions of space, :math:`x`, :math:`y`, and :math:`z`, due to the use of +``0 0 0``. The two ``velocity`` commands set the initial velocities +along :math:`x`, :math:`y`, and :math:`z` to 0 for the atoms of ``cnt_top`` and +``cnt_bot``, respectively. As a consequence of these last four commands, +the atoms of the edges will remain immobile during the simulation (or at least +they would if no other command was applied to them). + +.. admonition:: Note + :class: non-title-info + + The ``velocity set`` command imposes the velocity of a group of atoms at the start of a run but does + not enforce the velocity during the entire simulation. When ``velocity set`` is used in combination with + ``setforce 0 0 0``, as is the case here, the atoms won't feel any force during the entire simulation. + According to the Newton equation, no force means no acceleration, meaning that the initial velocity + will persist during the entire simulation, thus producing a constant velocity motion. + +Outputs +------- + +Next, to measure the strain and stress applied to the CNT, let us create a +variable for the distance :math:`L_\text{cnt}` between the two edges, +as well as a variable :math:`F_\text{cnt}` for the force applied on the edges: + +.. code-block:: lammps + + variable Lcnt equal xcm(cnt_top,x)-xcm(cnt_bot,x) + variable Fcnt equal f_mysf1[1]-f_mysf2[1] + +Here, the force is extracted from the fixes ``mysf1`` and ``mysf2`` +using ``f_`` , similarly to the use of ``v_`` to call a variable, +and ``c_`` to call a compute, as seen in :ref:`lennard-jones-label`. + +Let us also add a ``dump image`` command to visualize the system every 500 steps: + +.. code-block:: lammps + + dump viz all image 500 myimage-*.ppm element type size 1000 400 zoom 6 shiny 0.3 fsaa yes & + bond atom 0.8 view 0 90 box no 0.0 axes no 0.0 0.0 + dump_modify viz pad 9 backcolor white adiam 1 0.85 bdiam 1 1.0 + +Let us run a small equilibration step to bring the system to the required +temperature before applying any deformation. Replace the ``run 0 post no`` +command in **unbreakable.lmp** with the following lines: + +.. code-block:: lammps + + compute Tmid cnt_mid temp + thermo 100 + thermo_style custom step temp etotal v_Lcnt v_Fcnt + thermo_modify temp Tmid line yaml + + timestep 1.0 + run 5000 + +With the ``thermo_modify`` command, we specify to LAMMPS that the +temperature :math:`T_\mathrm{mid}` of the middle group, ``cnt_mid``, +must be outputted, instead of the temperature of the entire system. +This choice is motivated by the presence of frozen parts with an effective temperature of :math:`0~\text{K}`, +which makes the average temperature of the entire system less relevant. +The ``thermo_modify`` command also imposes the use of the YAML format that can easily be read by +Python (see below). + +Let us impose a constant velocity deformation on the CNT +by combining the ``velocity set`` command with previously defined +``fix setforce``. Add the following lines in the **unbreakable.lmp** +file, right after the last ``run 5000`` command: + +.. code-block:: lammps + + velocity cnt_top set 0.0005 0 0 + velocity cnt_bot set -0.0005 0 0 + + run 10000 + +The chosen velocity for the deformation is :math:`100\,\text{m/s}`, or +:math:`0.001\,\text{Å/fs}`. Run the simulation using LAMMPS. As can be seen +from the variable :math:`L_\text{cnt}`, the length +of the CNT increases linearly over time for :math:`t > 5\,\text{ps}`, +as expected from the imposed constant velocity. What you observe in the `Slide Show` +windows should resemble the figure below. + +.. figure:: figures/colored-edge-def-dark.png + :class: only-dark + :alt: Evolution of the CNT energy + +.. figure:: figures/colored-edge-def-light.png + :class: only-light + :alt: Evolution of the CNT energy + + The unbreakable CNT before (top) and after deformation (bottom). + +The total energy of the system +shows a non-linear increase with :math:`t` once the deformation starts, which is expected +from the typical dependency of bond energy with bond distance, +:math:`U_\text{b} = k_\text{b} \left( r - r_0 \right)^2`. + +.. figure:: figures/CNT-unbreakable-length-energy-dm.png + :class: only-dark + :alt: Evolution of the CNT energy + +.. figure:: figures/CNT-unbreakable-length-energy.png + :class: only-light + :alt: Evolution of the CNT energy + +.. container:: figurelegend + + Figure: a) Evolution of the length :math:`L_\text{cnt}` of the CNT with time. + The CNT starts deforming at :math:`t = 5\,\text{ps}`, and :math:`L_\text{cnt-0}` is the + CNT initial length. b) Evolution of the total energy :math:`E` of the system with time :math:`t`. + Here, the potential is OPLS-AA, and the CNT is unbreakable. + +Importing YAML log file into Python +----------------------------------- + +Let us import the simulation data into Python, and generate a stress-strain curve. +Here, the stress is defined as :math:`F_\text{cnt}/A_\text{cnt}`, +where :math:`A_\text{cnt} = \pi r_\text{cnt}^2` is the surface area of the +CNT, and :math:`r_\text{cnt}=5.2\,\text{Å}` the CNT radius. The strain is defined +as :math:`(L_\text{cnt}-L_\text{cnt-0})/L_\text{cnt-0}`, where :math:`L_\text{cnt-0}` is the initial CNT length. + +Right-click inside the ``Output`` window, and select +``Export YAML data to file``. Call the output **unbreakable.yaml**, and save +it within the same folder as the input files, where a Python script named |yaml_reader| should also +be located. When executed using Python, this .py file first imports +the **unbreakable.yaml** file. Then, a certain pattern is +identified and stored as a string character named ``docs``. The string is +then converted into a list, and :math:`F_\text{cnt}` and :math:`L_\text{cnt}` +are extracted. The stress and strain are then calculated, and the result +is saved in a data file named **unbreakable.dat** using +the NumPy ``savetxt`` function. ``thermo[0]`` can be used to access the +information from the first minimization run, and ``thermo[1]`` to access the +information from the second MD run. The data extracted from +the **unbreakable.yaml** file can then be used to plot the stress-strain curve. + +.. |yaml_reader| raw:: html + + unbreakable-yaml-reader.py + +.. figure:: figures/CNT-unbreakable-stress-strain-dm.png + :class: only-dark + :alt: Evolution of the carbon nanotube stress strain as calculated with LAMMPS + +.. figure:: figures/CNT-unbreakable-stress-strain.png + :class: only-light + :alt: Evolution of the carbon nanotube stress strain as calculated with LAMMPS + +.. container:: figurelegend + + Figure: Stress applied on the CNT during deformation, :math:`F_\text{cnt}/A_\text{cnt}`, + where :math:`F_\text{cnt}` is the force and :math:`A_\text{cnt}` the CNT surface area, + as a function of the strain, :math:`\Delta L_\text{cnt} = (L_\text{cnt}-L_\text{cnt-0})/L_\text{cnt-0}`, + where :math:`L_\text{cnt}` is the CNT length and :math:`L_\text{cnt-0}` the CNT initial length. + Here, the potential is OPLS-AA, and the CNT is unbreakable. + +Breakable bonds +=============== + +When using a conventional molecular force field, as we have just done, +the bonds between the atoms are non-breakable. Let us perform a similar +simulation and deform a small CNT again, but this time with a reactive +force field that allows bonds to break if the applied deformation is +large enough. + +Input file initialization +------------------------- + +Open the input named |breakable_lmp| +that should have been downloaded next to **unbreakable.lmp** during +the tutorial setup. There are only a few differences with the previous +input. First, the AIREBO force field requires the ``metal`` units +setting instead of ``real`` for OPLS-AA. A second difference is +the use of ``atom_style atomic`` instead of +``molecular``, since no explicit bond information is required with +AIREBO. The following commands are setting up the AIREBO force field: + +.. code-block:: lammps + + pair_style airebo 3.0 + pair_coeff * * CH.airebo C + +Here, |CH_airebo| is the file containing the parameters for AIREBO, +and must be placed next to **breakable.lmp**. + +.. |breakable_lmp| raw:: html + + breakable.lmp + +.. |CH_airebo| raw:: html + + CH.airebo + +.. admonition:: Note + :class: non-title-info + + With ``metal`` units, time values are in units of picoseconds + (:math:`10^{-12}\,\text{s}`) instead of femtoseconds (:math:`10^{-15}\,\text{s}`) in the case of + ``real`` units. It is important to keep this in mind when + setting parameters that are expressed in units containing time, such as + the timestep or the time constant of a thermostat, or velocities. + +Since bonds, angles, and dihedrals do not need to be explicitly set when +using AIREBO, some simplification must be made to the **.data** +file. The new **.data** file is named |breakable_data| +and must be placed within the same folder as the input file. Just like +**unbreakable.data**, the **breakable.data** contains the +information required for placing the atoms in the box, but no +bond/angle/dihedral information. Another difference between the +**unbreakable.data** and **breakable.data** files is that, +here, a larger distance of :math:`120~\text{Å}` was used for the box size along +the :math:`x`-axis, to allow for larger deformation of the CNT. + +.. |breakable_data| raw:: html + + breakable.data + +Start the simulation +-------------------- + +Here, let us perform a similar deformation as the previous one. +In **breakable.lmp**, replace the ``run 0 post no`` line with: + +.. code-block:: lammps + + fix mysf1 cnt_bot setforce 0 0 0 + fix mysf2 cnt_top setforce 0 0 0 + velocity cnt_bot set 0 0 0 + velocity cnt_top set 0 0 0 + + variable Lcnt equal xcm(cnt_top,x)-xcm(cnt_bot,x) + variable Fcnt equal f_mysf1[1]-f_mysf2[1] + + dump viz all image 500 myimage.*.ppm type type size 1000 400 zoom 4 shiny 0.3 adiam 1.5 box no 0.01 view 0 90 shiny 0.1 fsaa yes + dump_modify viz pad 5 backcolor white acolor 1 gray + + compute Tmid cnt_mid temp + thermo 100 + thermo_style custom step temp etotal v_Lcnt v_Fcnt + thermo_modify temp Tmid line yaml + + timestep 0.0005 + run 10000 + +Note the relatively small timestep of :math:`0.0005`\,ps (:math:`= 0.5`\,fs) used. Reactive force +fields like AIREBO usually require a smaller timestep than conventional ones. When running +**breakable.lmp** with LAMMPS, you can see that the temperature deviates +from the target temperature of :math:`300\,\text{K}` at the start of the equilibration, +but that after a few steps, it reaches the target value. + +.. admonition:: Note + :class: non-title-info + + Bonds cannot be displayed by the ``dump image`` when using + the ``atom_style atomic``, as it contains no bonds. A + tip for displaying bonds with the + present system using LAMMPS is provided at the end of the tutorial. + You can also use external tools like VMD or OVITO (see the + tip for tutorial 3). + +Launch the deformation +---------------------- + +After equilibration, let us set the velocity of the edges equal to +:math:`75~\text{m/s}` (or :math:`0.75~\text{Å/ps}`) and run for a longer duration than +previously. Add the following lines into **breakable.lmp**: + +.. code-block:: lammps + + velocity cnt_top set 0.75 0 0 + velocity cnt_bot set -0.75 0 0 + + run 30000 + +Run the simulation. Some bonds are expected to break before the end of the +simulation. + +.. figure:: figures/deformed-dark.png + :class: only-dark + :alt: Carbon nanotube deformed using LAMMPS + +.. figure:: figures/deformed-light.png + :class: only-light + :alt: Carbon nanotube deformed using LAMMPS + +.. container:: figurelegend + + Figure: Figure: CNT with broken bonds. This image was generated using + VMD :cite:`humphrey1996vmd` ``DynamicBonds`` representation. + +Looking at the evolution of the energy, one can see that the total +energy :math:`E` is initially increasing with the deformation. When bonds +break, the energy relaxes abruptly, as can be seen near :math:`t=32~\text{ps}`. +Using a similar script as previously, +i.e., |unbreakable_yaml_reader|, import the data into Python and generate +the stress-strain curve. The stress-strain +curve reveals a linear (elastic) regime where +:math:`F_\text{cnt} \propto \Delta L_\text{cnt}` for +:math:`\Delta L_\text{cnt} < 5\,\%`, and a non-linear (plastic) regime for +:math:`5\,\% < \Delta L_\text{cnt} < 25\,\%`. + +.. |unbreakable_yaml_reader| raw:: html + + unbreakable-yaml-reader.py + +.. figure:: figures/CNT-breakable-stress-energy-dm.png + :class: only-dark + :alt: Evolution of the CNT energy + +.. figure:: figures/CNT-breakable-stress-energy.png + :class: only-light + :alt: Evolution of the CNT energy + +.. container:: figurelegend + + Figure: Figure: a) Evolution of the total energy :math:`E` of the CNT with time :math:`t`. b) Stress applied on the CNT + during deformation, :math:`F_\text{cnt}/A_\text{cnt}`, + where :math:`F_\text{cnt}` is the force and :math:`A_\text{cnt}` the CNT surface area, + as a function of the strain, :math:`\Delta L_\text{cnt} = (L_\text{cnt}-L_\text{cnt-0}/L_\text{cnt-0})`, where + :math:`L_\text{cnt}` is the CNT length and :math:`L_\text{cnt-0}` the CNT initial length. + Here, the potential is AIREBO, and the CNT is breakable. + +Tip: bonds representation with AIREBO +------------------------------------- + +In the input file named |breakable_with_tip|, +which is an alternate solution for **breakable.lmp**, a trick is +used to represent bonds while using AIREBO. A detailed explanation of +the script is beyond the scope of the present tutorial. In short, the +trick is to use AIREBO with the ``molecular`` atom style, and use +the ``fix bond/break`` and ``fix bond/create/angle`` commands +to update the status of the bonds during the simulation: + +.. code-block:: lammps + + fix break all bond/break 1000 1 2.5 + fix form all bond/create/angle 1000 1 1 2.0 1 aconstrain 90.0 180 + +This *hack* works because AIREBO does not pay any attention to bonded +interactions and computes the bond topology dynamically inside the pair +style. Thus adding bonds of bond style ``zero`` does not add any +interactions but allows the visualization of them with ``dump image``. +It is, however, needed to change the ``special_bonds`` +setting to disable any neighbor list exclusions as they are common for +force fields with explicit bonds. + +.. code-block:: lammps + + bond_style zero + bond_coeff 1 1.4 + special_bonds lj/coul 1.0 1.0 1.0 + +.. |breakable_with_tip| raw:: html + + breakable-with-tip.lmp, diff --git a/docs/avatars/level2/polymer-in-water/PARM.lammps b/docs/sphinx/source/tutorial3/avatars/PARM.lammps similarity index 100% rename from docs/avatars/level2/polymer-in-water/PARM.lammps rename to docs/sphinx/source/tutorial3/avatars/PARM.lammps diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-dark.png b/docs/sphinx/source/tutorial3/avatars/PEG-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-dark.png rename to docs/sphinx/source/tutorial3/avatars/PEG-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-dark.webp b/docs/sphinx/source/tutorial3/avatars/PEG-dark.webp similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-dark.webp rename to docs/sphinx/source/tutorial3/avatars/PEG-dark.webp diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-light.png b/docs/sphinx/source/tutorial3/avatars/PEG-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-light.png rename to docs/sphinx/source/tutorial3/avatars/PEG-light.png diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-light.webp b/docs/sphinx/source/tutorial3/avatars/PEG-light.webp similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/PEG-light.webp rename to docs/sphinx/source/tutorial3/avatars/PEG-light.webp diff --git a/docs/avatars/level2/polymer-in-water/avatar-polymer-LAMMPS.png b/docs/sphinx/source/tutorial3/avatars/avatar-polymer-LAMMPS.png similarity index 100% rename from docs/avatars/level2/polymer-in-water/avatar-polymer-LAMMPS.png rename to docs/sphinx/source/tutorial3/avatars/avatar-polymer-LAMMPS.png diff --git a/docs/avatars/level2/polymer-in-water/input.lammps b/docs/sphinx/source/tutorial3/avatars/input.lammps similarity index 100% rename from docs/avatars/level2/polymer-in-water/input.lammps rename to docs/sphinx/source/tutorial3/avatars/input.lammps diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/main-dark.png b/docs/sphinx/source/tutorial3/avatars/main-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/main-dark.png rename to docs/sphinx/source/tutorial3/avatars/main-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/main-light.png b/docs/sphinx/source/tutorial3/avatars/main-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/main-light.png rename to docs/sphinx/source/tutorial3/avatars/main-light.png diff --git a/docs/avatars/level2/polymer-in-water/mix.data b/docs/sphinx/source/tutorial3/avatars/mix.data similarity index 100% rename from docs/avatars/level2/polymer-in-water/mix.data rename to docs/sphinx/source/tutorial3/avatars/mix.data diff --git a/docs/avatars/level2/polymer-in-water/state.vmd b/docs/sphinx/source/tutorial3/avatars/state.vmd similarity index 100% rename from docs/avatars/level2/polymer-in-water/state.vmd rename to docs/sphinx/source/tutorial3/avatars/state.vmd diff --git a/docs/sphinx/source/tutorial3/exercises.rst b/docs/sphinx/source/tutorial3/exercises.rst new file mode 100644 index 000000000..cb3fc67db --- /dev/null +++ b/docs/sphinx/source/tutorial3/exercises.rst @@ -0,0 +1,83 @@ +Going further with exercises +============================ + +Extract the radial distribution function +---------------------------------------- + +Extract the radial distribution functions (RDF or :math:`g(r)`) +between the oxygen atom of the water molecules +and the oxygen atom from the PEG molecule. Compare the rdf +before and after the force is applied to the PEG. + +.. figure:: figures/RDF-dark.png + :alt: RDF g(r) for water and peg + :class: only-dark + +.. figure:: figures/RDF-light.png + :alt: RDF g(r) for water and peg + :class: only-light + +.. container:: figurelegend + + Figure: Radial distribution function between the oxygen atoms + of water, as well as between the oxygen atoms of water and the + oxygen atoms of the PEG molecule. + +Note the difference in the structure of the water before and after +the PEG molecule is stretched. This effect is described in +the 2017 publication by Liese et al. :cite:`liese2017hydration`. + +Add salt to the system +---------------------- + +Realistic systems usually contain ions. Let us add some :math:`\text{Na}^+` and +:math:`\text{Cl}^-` ions to our current PEG-water system. + +Add some :math:`\text{Na}^+` and +:math:`\text{Cl}^-` ions to the mixture using the method +of your choice. :math:`\text{Na}^+` ions are +characterised by their mass :math:`m = 22.98\,\text{g/mol}`, +their charge :math:`q = +1\,e`, and Lennard-Jones +parameters, :math:`\epsilon = 0.0469\,\text{kcal/mol}` +and :math:`\sigma = 0.243\,\text{nm}`, +and :math:`\text{Cl}^-` ions by their +mass :math:`m = 35.453\,\text{g/mol}`, +charge :math:`q = -1\,e` and Lennard-Jones +parameters, :math:`\epsilon = 0.15\,\text{kcal/mol}`, +and :math:`\sigma = 0.4045\,\text{nm}`. + +.. figure:: figures/salt-exercise-dark.png + :alt: PEG in a NaCl solution + :class: only-dark + +.. figure:: figures/salt-exercise-light.png + :alt: PEG in a NaCl solution + :class: only-light + +.. container:: figurelegend + + Figure: A PEG molecule in the electrolyte with :math:`\text{Na}^+` ions in + purple and :math:`\text{Cl}^-` ions in cyan. + +Evaluate the deformation of the PEG +----------------------------------- + +Once the PEG is fully stretched, its structure differs from the +unstretched case. The deformation can be probed by extracting the typical +intra-molecular parameters, such as the typical angles of the dihedrals. + +Extract the histograms of the angular distribution of the PEG dihedrals +in the absence and the presence of stretching. + +.. figure:: figures/dihedral_angle-dark.png + :alt: PEG in a NaCl solution + :class: only-dark + +.. figure:: figures/dihedral_angle-light.png + :alt: PEG in a NaCl solution + :class: only-light + +.. container:: figurelegend + + Figure: Probability distribution for the dihedral angle :math:`\phi`, for a stretched + and for an unstretched PEG molecule. diff --git a/docs/sphinx/source/tutorial3/figures/PEG-density-dm.png b/docs/sphinx/source/tutorial3/figures/PEG-density-dm.png new file mode 100644 index 000000000..dd42e7cf8 Binary files /dev/null and b/docs/sphinx/source/tutorial3/figures/PEG-density-dm.png differ diff --git a/docs/sphinx/source/tutorial3/figures/PEG-density.png b/docs/sphinx/source/tutorial3/figures/PEG-density.png 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warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5f6dbb52", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"pull.log\")\n", + "timestep = 1 # fs\n", + "time_0 = log.get(\"Step\", run_num=0)*timestep/1000 # ps\n", + "rgyr_0 = log.get(\"c_rgyr\", run_num=0)\n", + "time_1 = log.get(\"Step\", run_num=1)*timestep/1000 # ps\n", + "rgyr_1 = log.get(\"c_rgyr\", run_num=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "dd7d3fc0", + "metadata": {}, + "outputs": [], + "source": [ + "f = open(path_data + \"pull.dat\", \"r\")\n", + "data = []\n", + "t = 0\n", + "for l in f:\n", + " if len(l.split(\" \")) == 4:\n", + " try:\n", + " n = np.int32(l.split(\" \")[0])\n", + " if n == 1:\n", + " t += 1\n", + " v = np.float32(l.split(\" \")[1])\n", + " b = np.int32(l.split(\" \")[2])\n", + " if b==1: # C-C-OE-C # b == 2: # O-C-C-O\n", + " data.append([t, v])\n", + " except:\n", + " pass\n", + "f.close()\n", + "data = np.array(data)\n", + "\n", + "all_n = np.int32(data[:,0])\n", + "before = []\n", + "after = []\n", + "for n in range(1,t):\n", + " x = data[all_n == n][:,1]\n", + " a, b = np.histogram(x, range=(0, 180), bins=50)\n", + " b = (b[1:]+b[:-1])/2\n", + " if n < 300:\n", + " before.append(a)\n", + " elif n > 340:\n", + " after.append(a)\n", + "histogram_before = np.mean(before, axis=0)\n", + "histogram_after = np.mean(after, axis=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7e9162cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"PEG-distance\"\n", + "\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " x = np.linspace(0, 11)\n", + " myplt.add_plot(x = x*0+15, y = x, type = \"plot\", linewidth_data = 1.5,\n", + " marker = \"--\", data_color = color0, markersize = 12)\n", + " myplt.add_plot(x = time_0-25, y = rgyr_0, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " myplt.add_plot(x = time_1-25, y = rgyr_1, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(xlabel = r'$t ~ \\mathrm{(ps)}$',\n", + " ylabel = r'$R_\\mathrm{gyr}~\\mathrm{(\\AA{})}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 32, 10), y_ticks=np.arange(0, 11, 2),\n", + " x_boundaries=(-2, 32), y_boundaries=(0, 11))\n", + "\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = np.linspace(0, 180, 50), y = histogram_before/np.sum(histogram_before), type = \"plot\",\n", + " linewidth_data = 3, marker = \"-\", data_color = color1, markersize = 12, data_label=r\"$\\mathrm{Without~force}$\")\n", + " myplt.add_plot(x = np.linspace(0, 180, 50), y = histogram_after/np.sum(histogram_after), type = \"plot\",\n", + " linewidth_data = 3, marker = \"-\", data_color = color2, markersize = 12, data_label=r\"$\\mathrm{With~force}$\")\n", + " myplt.complete_panel(xlabel = r'$\\phi (^\\circ)$',\n", + " ylabel = r'$p (\\phi)$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 185, 45), y_ticks=np.arange(0, 0.17, 0.05),\n", + " x_boundaries=(-10, 190), y_boundaries=(-0.01, 0.18))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/pulled_peg_dark.png b/docs/sphinx/source/tutorial3/figures/pulled_peg_dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/pulled_peg_dark.png rename to 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rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/solvatedPEG_dark.png rename to docs/sphinx/source/tutorial3/figures/solvatedPEG_dark.png diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/solvatedPEG_light.png b/docs/sphinx/source/tutorial3/figures/solvatedPEG_light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/solvatedPEG_light.png rename to docs/sphinx/source/tutorial3/figures/solvatedPEG_light.png diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/water-dark.png b/docs/sphinx/source/tutorial3/figures/water-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/water-dark.png rename to docs/sphinx/source/tutorial3/figures/water-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level2/polymer-in-water/water-light.png b/docs/sphinx/source/tutorial3/figures/water-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/polymer-in-water/water-light.png rename to docs/sphinx/source/tutorial3/figures/water-light.png diff --git a/docs/sphinx/source/tutorial3/figures/water.ipynb b/docs/sphinx/source/tutorial3/figures/water.ipynb new file mode 100644 index 000000000..e81c1ee4a --- /dev/null +++ b/docs/sphinx/source/tutorial3/figures/water.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5f6dbb52", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"water.log\")\n", + "timestep = 1 # fs\n", + "time = log.get(\"Step\", run_num=1)*timestep/1000 # ps\n", + "rho = log.get(\"v_rho\", run_num=1) # molecule / A3\n", + "temp = log.get(\"Temp\", run_num=1) # K" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "88e6cdea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"PEG-density\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = temp, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " x = np.linspace(0, 15)\n", + " myplt.add_plot(x = x, y = x*0+300, type = \"plot\", linewidth_data = 1.5,\n", + " marker = \"--\", data_color = color0, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$T ~ \\mathrm{(K)}$',\n", + " xlabel = r'$t~\\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 16, 5), y_ticks=np.arange(0, 360, 100),\n", + " x_boundaries=(-1, 16.2), y_boundaries=(-20, 370))\n", + "\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = rho, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " #x = np.linspace(0, 15)\n", + " #myplt.add_plot(x = x, y = x*0+996, type = \"plot\", linewidth_data = 1.5,\n", + " # marker = \"--\", data_color = color4, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$\\rho ~ (\\mathrm{kg/m}^3)$',\n", + " xlabel = r'$t~\\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 16, 5), y_ticks=np.arange(300, 1100, 200),\n", + " x_boundaries=(-1, 16.2), y_boundaries=(350, 1020))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorial3/introduction.rst b/docs/sphinx/source/tutorial3/introduction.rst new file mode 100644 index 000000000..a8f5b0cfa --- /dev/null +++ b/docs/sphinx/source/tutorial3/introduction.rst @@ -0,0 +1,28 @@ +.. figure:: avatars/PEG-dark.webp + :alt: Movie of a peg polymer molecule in water as simulated with LAMMPS + :height: 250 + :align: right + :class: only-dark + +.. figure:: avatars/PEG-light.webp + :alt: Movie of a peg polymer molecule in water as simulated with LAMMPS + :height: 250 + :align: right + :class: only-light + +The goal of this tutorial is to use LAMMPS to solvate a small +hydrophilic polymer (PEG - polyethylene glycol) in a reservoir of water. + +Once the water reservoir is properly equilibrated +at the desired temperature and pressure, the polymer molecule is added +and a constant stretching force is applied to both ends of the polymer. +The evolution of the polymer length is measured as a function of time. +The GROMOS 54A7 force field :cite:`schmid2011definition` is used for the +PEG, the SPC/Fw model :cite:`wu2006flexible` is used for the water, and +the long-range Coulomb interactions are solved using the PPPM +solver :cite:`luty1996calculating`. + +This tutorial was inspired by a +publication by Liese and coworkers, in which molecular dynamics +simulations are compared with force spectroscopy experiments, see +Ref. :cite:`liese2017hydration`. \ No newline at end of file diff --git a/docs/sphinx/source/tutorial3/polymer-in-water.rst b/docs/sphinx/source/tutorial3/polymer-in-water.rst new file mode 100644 index 000000000..77eebc345 --- /dev/null +++ b/docs/sphinx/source/tutorial3/polymer-in-water.rst @@ -0,0 +1,16 @@ +.. _all-atoms-label: + +Polymer in water +**************** + +.. container:: hatnote + + Solvating and stretching a small polymer molecule + +.. include:: introduction.rst +.. include:: ../shared/recommend-tutorial1.rst +.. include:: ../shared/cite.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst diff --git a/docs/sphinx/source/tutorial3/tutorial.rst b/docs/sphinx/source/tutorial3/tutorial.rst new file mode 100644 index 000000000..28ae9dc91 --- /dev/null +++ b/docs/sphinx/source/tutorial3/tutorial.rst @@ -0,0 +1,522 @@ +Preparing the water reservoir +============================= + +In this tutorial, the water reservoir is first prepared in the absence of the polymer. +A rectangular box of water is created and equilibrated at ambient temperature and +pressure. The SPC/Fw water model is used :cite:`wu2006flexible`, which is +a flexible variant of the rigid SPC (simple point charge) model :cite:`berendsen1981interaction`. +Create a file named **water.lmp**, and copy the following lines into it: + +.. code-block:: lammps + + units real + atom_style full + bond_style harmonic + angle_style harmonic + dihedral_style harmonic + pair_style lj/cut/coul/long 10 + kspace_style ewald 1e-5 + special_bonds lj 0.0 0.0 0.5 coul 0.0 0.0 1.0 angle yes + +.. admonition:: Optional: follow this tutorial using LAMMPS-GUI + :class: gui + + To set up this tutorial, select ``Start Tutorial 3`` from the + ``Tutorials`` menu of LAMMPS--GUI and follow the instructions. + The editor should display the content corresponding to **water.lmp**. + +With the unit style ``real``, masses are in g/mol, distances in Å, +time in fs, and energies in kcal/mol. With the ``atom_style full``, +each atom is a dot with a mass and a charge that can be linked +by bonds, angles, dihedrals, and/or impropers. The +``bond_style``, ``angle_style``, and +``dihedral_style`` commands define the potentials for the bonds, +angles, and dihedrals used in the simulation, here ``harmonic``. +With the ``pair_style`` named ``lj/cut/coul/long``, atoms +interact through both a Lennard-Jones (LJ) potential and Coulomb +interactions. The value of :math:`10\,\text{Å}` is the cutoff, and the +``ewald`` command defines the long-range solver for the Coulomb +interactions :cite:`ewald1921berechnung`. Finally, the +``special_bonds`` command, which was already seen in +:ref:`carbon-nanotube-label`, sets the LJ and Coulomb +weighting factors for the interaction between neighboring atoms. + +Let us create a 3D simulation box of dimensions :math:`6 \times 3 \times 3 \; \text{nm}^3`, +and make space for 8 atom types (2 for the water, 6 for the polymer), 7 bond types +(1 for the water, 6 for the polymer), 8 angle types (1 for the water, 7 for the polymer), +and 4 dihedral types (only for the polymer). Copy the following lines into **water.lmp**: + +.. code-block:: lammps + + region box block -30 30 -15 15 -15 15 + create_box 8 box bond/types 7 angle/types 8 dihedral/types 4 extra/bond/per/atom 3 & + extra/angle/per/atom 6 extra/dihedral/per/atom 10 extra/special/per/atom 14 + +The ``extra/x/per/atom`` commands reserve memory for adding bond topology +data later. We use the file |parameters_inc_3| +to set all the parameters (masses, interaction energies, bond equilibrium +distances, etc). Thus add to **water.lmp** the line: + +.. |parameters_inc_3| raw:: html + + parameters.inc + +.. code-block:: lammps + + include parameters.inc + +.. admonition:: Note + :class: non-title-info + + This tutorial uses type labels :cite:`gissinger2024type` to map each + numeric atom type to a string (see the **parameters.inc** file): + ``labelmap atom 1 OE 2 C 3 HC 4 H 5 CPos 6 OAlc 7 OW 8 HW`` + Therefore, the oxygen and hydrogen atoms of water (respectively types + 7 and 8) can be referred to as ``OW`` and ``HW``, respectively. Similar + maps are used for the bond types, angle types, and dihedral types. + +Let us create water molecules. To do so, let us import a molecule template called +**water.mol** and then randomly create 700 molecules. Add the following +lines into **water.lmp**: + +.. code-block:: lammps + + molecule h2omol water.mol + create_atoms 0 random 700 87910 NULL mol h2omol 454756 overlap 1.0 maxtry 50 + +The ``overlap 1.0`` option of the ``create_atoms`` command ensures +that no atoms are placed exactly in the same position, as this would cause the +simulation to crash. The ``maxtry 50`` asks LAMMPS to try at most 50 times +to insert the molecules, which is useful in case some insertion attempts are +rejected due to overlap. In some cases, depending on the system and the values +of ``overlap`` and ``maxtry``, LAMMPS may not create the desired number +of molecules. Always check the number of created atoms in the **log** file +(or in the ``Output`` window), where you should see: + +.. code-block:: bw + + Created 2100 atoms + +When LAMMPS fails to create the desired number of molecules, a WARNING +appears. The molecule template called |water_mol_3| must be downloaded and saved +next to **water.lmp**. This template contains the necessary +structural information of a water molecule, such as the number of atoms, +or the IDs of the atoms that are connected by bonds and angles. + +.. |water_mol_3| raw:: html + + water.mol + +.. figure:: figures/PEG-density-dm.png + :class: only-dark + :alt: Evolution of the water reservoir density + +.. figure:: figures/PEG-density.png + :class: only-light + :alt: Evolution of the water reservoir density + +.. container:: figurelegend + + Figure: a) Temperature, :math:`T`, of the water reservoir as a function of the + time, :math:`t`. The horizontal dashed line is the target temperature + of :math:`300 \text{K}`. b) Evolution of the system density, :math:`\rho`, with :math:`t` + +Then, let us organize the atoms of types OW and HW of the water +molecules in a group named ``H2O`` and perform a small energy +minimization. The energy minimization is mandatory here because of the +small ``overlap`` value of 1 Å chosen in the ``create_atoms`` +command. Add the following lines into **water.lmp**: + +.. code-block:: lammps + + group H2O type OW HW + minimize 1.0e-4 1.0e-6 100 1000 + reset_timestep 0 + +Resetting the step of the simulation to 0 using the +``reset_timestep`` command is optional. +It is used here because the number of iterations performed by the ``minimize`` +command is usually not a round number, since the minimization stops when one of +four criteria is reached. We will use ``fix npt`` to control the temperature +and pressure of the molecules with a Nosé-Hoover thermostat and barostat, +respectively :cite:`nose1984unified, hoover1985canonical, martyna1994constant`. +Add the following line into **water.lmp**: + +.. code-block:: lammps + + fix mynpt all npt temp 300 300 100 iso 1 1 1000 + +The ``fix npt`` allows us to impose both a temperature of :math:`300\,\text{K}` +(with a damping constant of :math:`100\,\text{fs}`), and a pressure of 1 atmosphere +(with a damping constant of :math:`1000\,\text{fs}`). With the ``iso`` keyword, +the three dimensions of the box will be re-scaled simultaneously. + +Let us output the system into images by adding the following commands to **water.lmp**: + +.. code-block:: lammps + + dump viz all image 250 myimage-*.ppm type type & + shiny 0.1 box no 0.01 view 0 90 zoom 3 size 1000 600 + dump_modify viz backcolor white & + acolor OW red acolor HW white & + adiam OW 3 adiam HW 1.5 + +Let us also extract the volume and density every 500 steps: + +.. code-block:: lammps + + variable myvol equal vol + variable myoxy equal count(H2O)/3 + variable NA equal 6.022e23 + variable Atom equal 1e-10 + variable M equal 0.018 + variable rho equal ${myoxy}*${M}/(v_myvol*${NA}*${Atom}^3) + thermo 500 + thermo_style custom step temp etotal v_myvol v_rho + +Here, several variables are defined and used for converting the units of the +density in kg/mol: The variable ``myoxy`` represents the number of +atoms divided by 3, which corresponds to the number of molecules, :math:`N_\text{H2O}`, +and the variable ``myrho`` is the density in kg/mol: + +.. math:: + + \rho = \dfrac{N_\text{H2O}}{V N_\text{A}}, + +where :math:`V` is the volume in :math:`\text{m}^3`, :math:`N_\text{A}` the Avogadro number, and +:math:`M = 0.018`\,kg/mol the molar mass of water. + +Finally, let us set the timestep to 1.0 fs, and run the simulation for 15 ps by +adding the following lines into **water.lmp**: + +.. code-block:: lammps + + timestep 1.0 + run 15000 + + write_restart water.restart + +The final state is saved in a binary file named **water.restart**. +Run the input using LAMMPS. The system reaches its equilibrium temperature +after just a few picoseconds, and its equilibrium density after approximately +10 picoseconds. + +.. figure:: figures/water-light.png + :alt: Water reservoir from molecular dynamics simulations + :class: only-light + +.. figure:: figures/water-dark.png + :alt: Water reservoir from molecular dynamics simulations + :class: only-dark + +.. container:: figurelegend + + Figure: The water reservoir after equilibration. Oxygen atoms are in red, and + hydrogen atoms are in white. + +.. admonition:: Note + :class: non-title-info + + The binary file created by the ``write_restart`` command contains the + complete state of the simulation, including atomic positions, velocities, and + box dimensions (similar to ``write_data``), but also the groups, + the compute, or the ``atom_style``. Use the ``Inspect Restart`` + option of the LAMMPS--GUI to vizualize the content saved in **water.restart**. + +Solvating the PEG in water +========================== + +Now that the water reservoir is equilibrated, we can safely add the PEG polymer +to the water. The PEG molecule topology was downloaded from the ATB repository +:cite:`malde2011automated, oostenbrink2004biomolecular`. It has a formula +:math:`\text{C}_{16}\text{H}_{34}\text{O}_{9}`, and the parameters are taken from +the GROMOS 54A7 force field :cite:`schmid2011definition`. + +.. figure:: figures/singlePEG-light.png + :alt: PEG in vacuum as simulated with LAMMPS + :class: only-light + +.. figure:: figures/singlePEG-dark.png + :alt: PEG in vacuum as simulated with LAMMPS + :class: only-dark + +.. container:: figurelegend + + Figure: The PEG molecule with carbon atoms in gray, oxygen atoms in red, + and hydrogen atoms in white. + +Open the file named **merge.lmp** that was downloaded +alongside **water.lmp** during the tutorial setup. It only contain one line: + +.. code-block:: lammps + + read_restart water.restart + +Most of the commands that were initially present in **water.lmp**, such as +the ``units`` of the ``atom_style`` commands do not need to be repeated, +as they were saved within the **.restart** file. There is also no need to +re-include the parameters from the **.inc** file. The ``kspace_style`` +command, however, is not saved by the ``write_restart`` command and must be +repeated. Since Ewald summation is not the most efficient choice for such dense +system, let us use PPPM (for particle-particle particle-mesh) for the rest +of the tutorial. Add the following command to **merge.lmp**: + +.. code-block:: lammps + + kspace_style pppm 1e-5 + +Using the molecule template for the polymer called |peg_mol_3|, +let us create a single molecule in the middle of the box by adding the following +commands to **merge.lmp**: + +.. |peg_mol_3| raw:: html + + peg.mol + +.. code-block:: lammps + + molecule pegmol peg.mol + create_atoms 0 single 0 0 0 mol pegmol 454756 + +Let us create a group for the atoms of the PEG (the previously created +group H2O was saved by the restart and can be omitted): + +.. code-block:: lammps + + group PEG type C CPos H HC OAlc OE + +Water molecules that are overlapping with the PEG must be deleted to avoid future +crashing. Add the following line into **merge.lmp**: + +.. code-block:: lammps + + delete_atoms overlap 2.0 H2O PEG mol yes + +Here the value of 2.0 Å for the overlap cutoff was fixed arbitrarily and can +be chosen through trial and error. If the cutoff is too small, the simulation will +crash because atoms that are too close to each other undergo forces +that can be extremely large. If the cutoff is too large, too many water +molecules will unnecessarily be deleted. + +Let us use the ``fix npt`` to control the temperature, as +well as the pressure by allowing the box size to be rescaled along the :math:`x`-axis: + +.. code-block:: lammps + + fix mynpt all npt temp 300 300 100 x 1 1 1000 + + +Let us also use the ``recenter`` command to always keep the PEG at +the position :math:`(0, 0, 0)`: + +.. code-block:: lammps + + fix myrct PEG recenter 0 0 0 shift all + +Note that the ``recenter`` command has no impact on the dynamics, +it simply repositions the frame of reference so that any drift of the +system is ignored, which can be convenient for visualizing and analyzing +the system. + +Let us create images of the systems: + +.. code-block:: lammps + + dump viz all image 250 myimage-*.ppm type type size 1100 600 box no 0.1 shiny 0.1 view 0 90 zoom 3.3 fsaa yes bond atom 0.8 + dump_modify viz backcolor white acolor OW red adiam OW 0.2 acolor OE darkred adiam OE 2.6 acolor HC white adiam HC 1.4 & + acolor H white adiam H 1.4 acolor CPos gray adiam CPos 2.8 acolor HW white adiam HW 0.2 acolor C gray adiam C 2.8 & + acolor OAlc darkred adiam OAlc 2.6 + thermo 500 + +Finally, to perform a short equilibration and save the final state to +a **.restart** file, add the following lines to the input: + +.. code-block:: lammps + + timestep 1.0 + run 10000 + + write_restart merge.restart + +Run the simulation using LAMMPS. From the outputs, you can make +sure that the temperature remains close to the +target value of :math:`300~\text{K}` throughout the entire simulation, and that +the volume and total energy are almost constant, indicating +that the system was in a reasonable configuration from the start. + +.. figure:: figures/solvatedPEG_light.png + :alt: PEG in water as simulated with LAMMPS + :class: only-light + +.. figure:: figures/solvatedPEG_dark.png + :alt: PEG in water as simulated with LAMMPS + :class: only-dark + +.. container:: figurelegend + + Figure : The PEG molecule solvated in water. Water is represented as a + transparent field for clarity. + +Stretching the PEG molecule +=========================== + +Here, a constant force is applied to both ends of the PEG molecule until it +stretches. Open the file named **pull.lmp**, which +only contains two lines: + +.. code-block:: lammps + + kspace_style pppm 1e-5 + read_restart merge.restart + +Next, we'll create new atom groups, each containing a single oxygen atom. The atoms of type OAlc +correspond to the hydroxyl (alcohol) group oxygen atoms located at the ends +of the PEG molecule, which we will use to apply the force. Add the +following lines to **pull.lmp**: + +.. code-block:: lammps + + group ends type OAlc + variable xcm equal xcm(ends,x) + variable oxies atom type==label2type(atom,OAlc) + variable end1 atom v_oxies*(x>v_xcm) + variable end2 atom v_oxies*(xpull-with-tip.lmp + +.. code-block:: lammps + + dump mydmp all atom 1000 pull.lammpstrj + +Running the **pull-with-tip.lmp** file using LAMMPS will generate a trajectory file +named **pull.lammpstrj**, which can be opened in OVITO or VMD. + +.. admonition:: Note + :class: non-title-info + + Since the trajectory dump file does not contain information about + topology and elements, it is usually preferred to first write out a + data file and import it directly (in the case of OVITO) or convert it + to a PSF file (for VMD). This allows the topology to be loaded before + *adding* the trajectory file to it. When using LAMMPS--GUI, + this process can be automated through the ``View in OVITO`` or + ``View in VMD`` options in the ``Run`` menu. Afterwards + only the trajectory dump needs to be added. \ No newline at end of file diff --git a/docs/avatars/level2/nanosheared-electrolyte/GROUP.lammps b/docs/sphinx/source/tutorial4/avatars/GROUP.lammps similarity index 100% rename from docs/avatars/level2/nanosheared-electrolyte/GROUP.lammps rename to docs/sphinx/source/tutorial4/avatars/GROUP.lammps diff --git a/docs/avatars/level2/nanosheared-electrolyte/PARM.lammps b/docs/sphinx/source/tutorial4/avatars/PARM.lammps similarity index 100% rename from docs/avatars/level2/nanosheared-electrolyte/PARM.lammps rename to docs/sphinx/source/tutorial4/avatars/PARM.lammps diff --git a/docs/avatars/level2/nanosheared-electrolyte/RigidH2O.txt b/docs/sphinx/source/tutorial4/avatars/RigidH2O.txt similarity index 100% rename from docs/avatars/level2/nanosheared-electrolyte/RigidH2O.txt rename to docs/sphinx/source/tutorial4/avatars/RigidH2O.txt diff --git a/docs/avatars/level2/nanosheared-electrolyte/avatar-electrolyte-LAMMPS.png b/docs/sphinx/source/tutorial4/avatars/avatar-electrolyte-LAMMPS.png similarity index 100% rename from docs/avatars/level2/nanosheared-electrolyte/avatar-electrolyte-LAMMPS.png rename to docs/sphinx/source/tutorial4/avatars/avatar-electrolyte-LAMMPS.png diff --git a/docs/avatars/level2/nanosheared-electrolyte/input.lammps b/docs/sphinx/source/tutorial4/avatars/input.lammps similarity index 100% rename from docs/avatars/level2/nanosheared-electrolyte/input.lammps rename to docs/sphinx/source/tutorial4/avatars/input.lammps diff --git a/docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/nanoconfined-electrolyte-dark.png b/docs/sphinx/source/tutorial4/avatars/nanoconfined-electrolyte-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/nanoconfined-electrolyte-dark.png rename to docs/sphinx/source/tutorial4/avatars/nanoconfined-electrolyte-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/nanoconfined-electrolyte-light.png b/docs/sphinx/source/tutorial4/avatars/nanoconfined-electrolyte-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/nanoconfined-electrolyte-light.png rename to docs/sphinx/source/tutorial4/avatars/nanoconfined-electrolyte-light.png diff --git a/docs/avatars/level2/nanosheared-electrolyte/state.vmd b/docs/sphinx/source/tutorial4/avatars/state.vmd similarity index 100% rename from docs/avatars/level2/nanosheared-electrolyte/state.vmd rename to docs/sphinx/source/tutorial4/avatars/state.vmd diff --git a/docs/sphinx/source/tutorial4/exercises.rst b/docs/sphinx/source/tutorial4/exercises.rst new file mode 100644 index 000000000..5d49149c1 --- /dev/null +++ b/docs/sphinx/source/tutorial4/exercises.rst @@ -0,0 +1,32 @@ +Going further with exercises +============================ + + +Induce a Poiseuille flow +------------------------ + +Instead of inducing a shearing of the fluid using the walls, +induce a net flux of the liquid in the direction tangential +to the walls. The walls must be kept immobile. + +Extract the velocity profile, and make sure that the +resulting velocity profile is consistent with the Poiseuille equation, +which can be derived from the Stokes equation :math:`\eta \nabla \textbf{v} = - \textbf{f} \rho` +where :math:`f` is the applied force, +:math:`\rho` is the fluid density, +:math:`\eta` is the fluid viscosity. + +.. figure:: figures/shearing-poiseuille-light.png + :alt: Velocity of the fluid forming a Poiseuille flow + :class: only-light + +.. figure:: figures/shearing-poiseuille-dark.png + :alt: Velocity of the fluid forming a Poiseuille flow + :class: only-dark + +.. container:: figurelegend + + Figure: Velocity profiles of the water molecules along the *z* axis (disks). + The line is the Poiseuille equation. + +An important step is to choose the proper value for the additional force. diff --git a/docs/sphinx/source/tutorial4/figures/NANOSHEAR-equilibration-dm.png 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000000000..060c0a5ad --- /dev/null +++ b/docs/sphinx/source/tutorial4/figures/equilibrate.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "7306ba82", + "metadata": {}, + "outputs": [], + "source": [ + "def sliding_average(data, window_size):\n", + " \"\"\"Calculate the sliding (moving) average of a dataset with edge handling.\"\"\"\n", + " pad_width = window_size // 2\n", + " padded_data = np.pad(data, pad_width, mode='edge')\n", + " smoothed_data = np.convolve(padded_data, np.ones(window_size) / window_size, mode='valid')\n", + " return smoothed_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5f6dbb52", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"equilibrate.log\")\n", + "\n", + "timestep = 1 # fs\n", + "time = log.get(\"Step\", run_num=1)*timestep/1000 # ps\n", + "Press = log.get(\"Press\", run_num=1)/1000 # katm\n", + "deltaz = log.get(\"v_deltaz\", run_num=1)/10 # nm " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5c269fb8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"NANOSHEAR-equilibration\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + "\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = Press, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " sPress = sliding_average(Press, 10)\n", + " stime = sliding_average(time, 10)\n", + " myplt.add_plot(x = stime, y = sPress, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$p ~ \\mathrm{(katm)}$',\n", + " xlabel = r'$t~\\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 31, 10), y_ticks=np.arange(-20, -10, 2),\n", + " x_boundaries=(-5, 35), y_boundaries=(-20, -12))\n", + "\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = deltaz, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " sdeltaz = sliding_average(deltaz, 10)\n", + " stime = sliding_average(time, 10)\n", + " myplt.add_plot(x = stime, y = sdeltaz, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$\\Delta z ~ \\mathrm{(nm)}$',\n", + " xlabel = r'$t~\\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 31, 10), y_ticks=np.arange(2.6, 3.3, 0.2),\n", + " x_boundaries=(-5, 35), y_boundaries=(2.5, 3.3))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/equilibration-article.png b/docs/sphinx/source/tutorial4/figures/equilibration-article.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/equilibration-article.png rename to docs/sphinx/source/tutorial4/figures/equilibration-article.png diff --git 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b/docs/sphinx/source/tutorial4/figures/shearing.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "30a4528c", + "metadata": {}, + "outputs": [], + "source": [ + "data = np.loadtxt(path_data + \"shearing-water.dat\", skiprows = 4)\n", + "vx = data[:,4]*1e5 # m/s\n", + "z = data[:,1]/10 # nm\n", + "rho = data[:,3]\n", + "\n", + "data = np.loadtxt(path_data + \"shearing-ions.dat\", skiprows = 4)\n", + "vxi = data[:,4]*1e5 # m/s\n", + "zi = data[:,1]/10 # nm\n", + "rhoi = data[:,3]\n", + "\n", + "data = np.loadtxt(path_data + \"shearing-wall.dat\", skiprows = 4)\n", + "vxw = data[:,4]*1e5 # m/s\n", + "zw = data[:,1]/10 # nm\n", + "rhow = data[:,3]\n", + "\n", + "rhof = rho+rhoi\n", + "vxf = (vx+vxi)/2" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "d6b20cb2", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5nq8fH6vjb9Xt12g03ty5uFvszLIs0jQ9eGzH7HIEAADKT+EPAACAo5nP5zEajaLZbMbNzc2nHvtYHX+raTr36Vx8bdtVAfGQNj/zZhEUAAA4D8dZSR0AAICzl6Zp3N/fR61W+7Si32ax6xiFvyRJ1tNojsfjGI/H797XbDaLPM+POiXpsfYNAACUkysAAAAADi7Lsri7u4uIiJubm08rQB27421zbb5arbb3f7tW3YOHcuzCJwAAUG46/gAAADioPM/j/v4+iqKIf/75J1qt1sniOKQ0TSPLsoiI+P79+7s+17///rveR8Rz4W8wGBwsxlU3YoTCHwAAnCNXAQAAABzU3d1dLJfL+PbtW3Q6nU899maxa7MIdgirNfmazea7i5m7Rb7lchnz+fzDsa1Y1w8AAM6bwh8AAAAH8/DwEFmWRa/XO2gn23scsgiW53nMZrOIiOj3++/eT7fbfTHl56qgeAirYudr04oCAADVp/AHAADAQYxGo5jNZtFut2M4HJ4sjmbzeVWLQ3b8bRbner3eh/bV7Xa3ni8Wi4N36jUajYPuDwAA+BoU/gAAAPiwyWQSSZJEo9GI6+vrN70nz/OYTqcHj2VV+DukJEki4uNFv4jXOwYP0fW3WTw8xu8AAAAoP4U/AAAAPmQ6ncZ4PI5arRb//PPP1jp7f5Ikybqgdkib3W5Zln14f9PpdN09eIjCX7PZfFGYO8TvYblcrh/r+AMAgPOk8AcAAMC7pWkav379ioiIm5ubvTrNptNpdDqdg8fUarXWj9M0/fD+Vt14tVpta98fsdv1VxTFh4t/m0XOY/xeAQCA8lP4AwAA4F2yLIu7u7uIiLi6uopWqxVZlsV8Po/5fP7Hbrs0TSPLsoMV0jZtFr0+WvibTqfrz3HIYtrFxcWL1x4fHz+0z83PqvAHAADnyaT/AAAA7C3P87i9vY2iKKLRaESSJOvOv13NZjPa7Xa02+1oNpuR53mMRqOIOE6Bql6vR6PRiOVy+aHC32ach1av16NWq62nEI147vp7fHyMy8vLd+1z9Vmt7wcAAOfL1QAAAAB7u7u7Wxf9Wq1WNBqNdVEvTdOt9eayLIssy15MZXnMdeg6nU4kSRKLxWKv92VZFrPZLJbL5dbafhERs9ks/v3332i329FoNKLT6ezVsZgkSWRZFkVRxHw+39r3ytPTU6RpGq1WK7rd7l77X31W3X4AAHC+FP4AAADYW71ej6urq+h2u6/+fFUAnEwmvy2+HWOaz5Ver7cuNK4KaW+xXC5jPB6/eL1Wq61/vlnA3Ocz/Kl7cLX/iOcC3up39tb9z+fz9eNer/fmmAAAgGqpFa/dYggAAAAHkqZpTKfTmM/nsVwuoyiKaDabcXl5edTutJ8/f8ZyuYx+v//u6TO/itFoFEmSRLPZjP/85z+nDgcAADgRHX8AAAAcVavVOmp33+/0er0Yj8eRJEnlC3/T6TQiIvr9/okjAQAATql+6gAAAADgGFZTXq7W1KuqzfUCLy4uThwNAABwSgp/AAAAVFK9Xl8X/yaTyYmjOZ7VZ+v3+1Gvu8wHAIBz5ooAAACAyvr27VtERCwWi8iy7MTRHF6aprFYLKJWq8VgMDh1OAAAwIkp/AEAAFBZ9Xo9hsNhRESMx+MTR3N4j4+PERFxeXmp2w8AAFD4AwAAoNp6vV40m82YzWaVWutvOp3GYrGIdru9ntIUAAA4bwp/AAAAVN7NzU3UarV4eHg4dSgHked5jEajqNVqcX19fepwAACAklD4AwAAoPLq9XpcX19HURSVKP6NRqMoiiJubm5M8QkAAKy5OgAAAOAsdDqduLq6itlstl4b7yt6fHyM2WwWV1dX0Wq1Th0OAABQIs1TBwAAAACfpdvtRlEUMRqNotlsfrm18ZIkiaenp7i6uoput3vqcAAAgJJR+AMAAOCs9Hq9aDQa8fDwEBcXF19mqsw8z2MymcT37991+gEAAK+qFUVRnDoIAAAAAAAA4GO+xm2NAAAAAAAAwB8p/AEAAAAAAEAFKPwBAAAAAABABSj8AQAAAAAAQAUo/AEAAAAAAEAFKPwBAAAAAABABSj8AQAAAAAAQAUo/AEAAAAAAEAFKPwBAAAAAABABSj8AQAAAAAAQAX8f66e030ex16FAAAAAElFTkSuQmCC", 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"NANOSHEAR-profiles\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1)\n", + "\n", + " # Panel a\n", + " #myplt.add_panel()\n", + " #myplt.add_plot(x = z[rho>0], y = rho[rho>0], type = \"plot\", linewidth_data = 3,\n", + " # marker = \"o-\", data_color = color2, markersize = 12)\n", + " #myplt.complete_panel(ylabel = r'$\\rho ~ \\mathrm{(g/mol/\\AA{}^3)}$',\n", + " # xlabel = None, xpad = 10, legend=True, handlelength_legend=1)\n", + " #myplt.set_boundaries(x_ticks=np.arange(-1.5, 1.6, 0.5), y_ticks=np.arange(0, 1.81, 0.4),\n", + " # x_boundaries=(-1.6, 1.6), y_boundaries=(-0.2, 1.8))\n", + "\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = z[rho>0], y = vx[rho>0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"o\", data_color = color2, markersize = 12, data_label=r\"$\\mathrm{water}$\")\n", + " myplt.add_plot(x = zw[rhow>0], y = vxw[rhow>0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"s\", data_color = color1, markersize = 12, data_label=r\"$\\mathrm{wall}$\")\n", + " myplt.complete_panel(ylabel = r'$v_x ~ \\mathrm{(m/s)}$',\n", + " xlabel = r'$z~\\mathrm{(\\AA{})}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-1.5, 1.6, 0.5), y_ticks=np.arange(-30, 35, 10),\n", + " x_boundaries=(-1.6, 1.6), y_boundaries=(-25, 25))\n", + "\n", + " # Print figure\n", + " # myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + }, + { + "cell_type": "markdown", + "id": "526a6f75", + "metadata": {}, + "source": [ + "#### The next lines estimate the viscosity from the stress applied by the fluid on the walls." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "9e28e501", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stress= 62861550.60553605 Pa\n", + "viscosity= 62861550.60553605 Pa s\n" + ] + } + ], + "source": [ + "log = lammps_logfile.File(\"shearing.log\")\n", + "\n", + "A = 6e-18 # m2 -- (12.12*2e-10)**2 -- surface area of the wall\n", + "tau_1 = np.mean(log.get(\"f_mysf1[1]\", run_num=0)[200:]) # force on wall in kcal/mol/A\n", + "tau_2 = np.mean(log.get(\"f_mysf2[1]\", run_num=0)[200:]) # force on wall kcal/mol/A\n", + "total_stress = (tau_2-tau_1)*4184/6.022e23*1e10/A # stress in Newton\n", + "print(\"stress=\", total_stress, \"Pa\") # Pa\n", + "gamma_dot = 20e9 # s-1\n", + "eta = total_stress/gamma_dot\n", + "print(\"viscosity=\", total_stress, \"Pa s\") # Pa\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/shearing.png b/docs/sphinx/source/tutorial4/figures/shearing.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/shearing.png rename to docs/sphinx/source/tutorial4/figures/shearing.png diff --git a/docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/systemcreation-dark.png b/docs/sphinx/source/tutorial4/figures/systemcreation-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/systemcreation-dark.png rename to docs/sphinx/source/tutorial4/figures/systemcreation-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/systemcreation-light.png b/docs/sphinx/source/tutorial4/figures/systemcreation-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/systemcreation-light.png rename to docs/sphinx/source/tutorial4/figures/systemcreation-light.png diff --git a/docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/velocity-article.png b/docs/sphinx/source/tutorial4/figures/velocity-article.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level2/nanosheared-electrolyte/velocity-article.png rename to docs/sphinx/source/tutorial4/figures/velocity-article.png diff --git a/docs/sphinx/source/tutorial4/introduction.rst b/docs/sphinx/source/tutorial4/introduction.rst new file mode 100644 index 000000000..50a4c2363 --- /dev/null +++ b/docs/sphinx/source/tutorial4/introduction.rst @@ -0,0 +1,26 @@ +.. figure:: avatars/nanoconfined-electrolyte-dark.png + :height: 250 + :alt: Electrolyte nano-confined in a slit pore + :class: only-dark + :align: right + +.. figure:: avatars/nanoconfined-electrolyte-light.png + :height: 250 + :alt: Electrolyte nano-confined in a slit pore + :class: only-light + :align: right + +The objective of this tutorial is to simulate an electrolyte +nanoconfined and sheared between two walls. The density +and velocity profiles of the fluid in the direction normal to the walls are +extracted to highlight the effect of confining a fluid on its local properties. +This tutorial demonstrates key concepts of combining a fluid and a solid in +the same simulation. A major difference from the previous tutorial, +:ref:`all-atoms-label`, is that here a rigid four-point +water model named TIP4P/2005 is used :cite:`abascal2005general`. + +.. admonition:: Note + :class: non-title-info + + Four-point water models such as TIP4P/2005 are widely used as they offer a + good compromise between accuracy and computational cost :cite:`kadaoluwa2021systematic`. diff --git a/docs/sphinx/source/tutorial4/nanosheared-electrolyte.rst b/docs/sphinx/source/tutorial4/nanosheared-electrolyte.rst new file mode 100644 index 000000000..5a3fb80c9 --- /dev/null +++ b/docs/sphinx/source/tutorial4/nanosheared-electrolyte.rst @@ -0,0 +1,16 @@ +.. _sheared-confined-label: + +Nanosheared electrolyte +*********************** + +.. container:: hatnote + + Aqueous NaCl solution sheared by two walls + +.. include:: introduction.rst +.. include:: ../shared/recommend-tutorial1.rst +.. include:: ../shared/cite.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst diff --git a/docs/sphinx/source/tutorial4/tutorial.rst b/docs/sphinx/source/tutorial4/tutorial.rst new file mode 100644 index 000000000..807ca1ccd --- /dev/null +++ b/docs/sphinx/source/tutorial4/tutorial.rst @@ -0,0 +1,605 @@ + +System preparation +================== + +The fluid and walls must first be generated, followed by equilibration at the +desired temperature and pressure. + +System generation +----------------- + +Create a folder if needed and +place the initial input file, **create.lmp**, into it. Then, open the +file in a text editor of your choice, and copy the following into it: + +.. code-block:: lammps + + boundary p p f + units real + atom_style full + bond_style harmonic + angle_style harmonic + pair_style lj/cut/tip4p/long O H O-H H-O-H 0.1546 12.0 + kspace_style pppm/tip4p 1.0e-5 + kspace_modify slab 3.0 + + +.. admonition:: If you are using LAMMPS-GUI + :class: gui + + To begin this tutorial, select ``Start Tutorial 4`` from the + ``Tutorials`` menu of LAMMPS--GUI and follow the instructions. + The editor should display the following content corresponding to **create.lmp** + +These lines are used to define the most basic parameters, including the +atom, bond, and angle styles, as well as interaction +potential. Here, ``lj/cut/tip4p/long`` imposes a Lennard-Jones potential with +a cut-off at :math:`12\,\text{Å}` and a long-range Coulomb potential. + +So far, the commands are relatively similar to those in the previous tutorial, +:ref:`all-atoms-label`, with two major differences: the use +of ``lj/cut/tip4p/long`` instead of ``lj/cut/coul/long``, and ``pppm/tip4p`` +instead of ``pppm``. When using ``lj/cut/tip4p/long`` and ``pppm/tip4p``, +the interactions resemble the conventional Lennard-Jones and Coulomb interactions, +except that they are specifically designed for the four-point water model. As a result, +LAMMPS automatically creates a four-point water molecule, assigning type O +atoms as oxygen and type H atoms as hydrogen. The fourth massless atom (M) of the +TIP4P water molecule does not have to be defined explicitly, and the value of +:math:`0.1546\,\text{Å}` corresponds to the O-M distance of the +TIP4P-2005 water model :cite:`abascal2005general`. All other atoms in the simulation +are treated as usual, with long-range Coulomb interactions. Another novelty, here, is +the use of ``kspace modify slab 3.0`` that is combined with the non-periodic +boundaries along the :math:`z` coordinate: ``boundary p p f``. With the ``slab`` +option, the system is treated as periodical along :math:`z`, but with an empty volume inserted +between the periodic images of the slab, and the interactions along :math:`z` effectively turned off. + +Let us create the box and the label maps by adding the following lines to **create.lmp**: + +.. code-block:: lammps + + lattice fcc 4.04 + region box block -3 3 -3 3 -5 5 + create_box 5 box bond/types 1 angle/types 1 extra/bond/per/atom 2 extra/angle/per/atom 1 & + extra/special/per/atom 2 + labelmap atom 1 O 2 H 3 Na+ 4 Cl- 5 WALL + labelmap bond 1 O-H + labelmap angle 1 H-O-H + +The ``lattice`` command defines the unit cell. Here, the face-centered cubic (fcc) lattice +with a scale factor of 4.04 has been chosen for the future positioning of the atoms +of the walls. The ``region`` command defines a geometric region of space. By choosing +:math:`\text{xlo}=-3` and :math:`\text{xlo}=3`, and because we have previously chosen a lattice with a scale +factor of 4.04, the region box extends from :math:`-12.12~\text{Å}` to :math:`12.12~\text{Å}` +along the :math:`x` direction. The ``create_box`` command creates a simulation box with +5 types of atoms: the oxygen and hydrogen of the water molecules, the two ions (:math:`\text{Na}^+`, +:math:`\text{Cl}^-`), and the atoms from the walls. The simulation contains 1 type of bond +and 1 type of angle (both required by the water molecules). +The parameters for these bond and angle constraints will be given later. The ``extra (...)`` +keywords are for memory allocation. Finally, the ``labelmap`` commands assign +alphanumeric type labels to each numeric atom type, bond type, and angle type. + +Now, we can add atoms to the system. First, let us create two sub-regions corresponding +respectively to the two solid walls, and create a larger region from the union of the +two regions. Then, let us create atoms of type WALL within the two regions. Add the +following lines to **create.lmp**: + +.. code-block:: lammps + + region rbotwall block -3 3 -3 3 -4 -3 + region rtopwall block -3 3 -3 3 3 4 + region rwall union 2 rbotwall rtopwall + create_atoms WALL region rwall + +Atoms will be placed in the positions of the previously defined lattice, thus +forming fcc solids. + +To add the water molecules, the molecule template called |water_mol_4| +must be located next to **create.lmp**. The template contains all the +necessary information concerning the water molecule, such as atom positions, +bonds, and angles. Add the following lines to **create.lmp**: + +.. |water_mol_4| raw:: html + + water.mol + +.. code-block:: lammps + + region rliquid block INF INF INF INF -2 2 + molecule h2omol water.mol + create_atoms 0 region rliquid mol h2omol 482793 + +Within the last three lines, a ``region`` named ``rliquid`` is +created based on the last defined lattice, ``fcc 4.04``. ``rliquid`` +will be used for depositing the water molecules. The ``molecule`` command +opens up the molecule template called **water.mol**, and names the +associated molecule ``h2omol``. The new molecules are placed on the +``fcc 4.04`` lattice by the ``create_atoms`` command. The first +parameter is 0, meaning that the atom IDs from the **water.mol** file +will be used. The number ``482793`` is a seed that is required by LAMMPS, +it can be any positive integer. + +Finally, let us create 30 ions (15 :math:`\text{Na}^+` and 15 :math:`\text{Cl}^-`) in between +the water molecules, by adding the following commands to **create.lmp**: + +.. code-block:: lammps + + create_atoms Na+ random 15 5802 rliquid overlap 0.3 maxtry 500 + create_atoms Cl- random 15 9012 rliquid overlap 0.3 maxtry 500 + set type Na+ charge 1 + set type Cl- charge -1 + +Each ``create_atoms`` command will add 15 ions at random positions +within the ``rliquid`` region, ensuring that there is no ``overlap`` +with existing molecules. Feel free to increase or decrease the salt concentration +by changing the number of desired ions. To keep the system charge neutral, +always insert the same number of :math:`\text{Na}^+` and :math:`\text{Cl}^-`, unless there +are other charges in the system. The charges of the newly added ions are specified +by the two ``set`` commands. + +Before starting the simulation, we need to define the parameters of the +simulation: the mass of the 5 atom types (O, H, :math:`\text{Na}^+`, :math:`\text{Cl}^-`, +and wall), the pairwise interaction parameters (in this case, for the +Lennard-Jones potential), and the bond and angle parameters. Copy the following +lines into **create.lmp**: + +.. code-block:: lammps + + include parameters.inc + include groups.inc + +Both |parameters_inc_4| and |groups_inc_4| files +must be located next to **create.lmp**. The **parameters.inc** file contains the masses, as follows: + +.. |parameters_inc_4| raw:: html + + parameters.inc + +.. |groups_inc_4| raw:: html + + groups.inc + +.. code-block:: lammps + + mass O 15.9994 + mass H 1.008 + mass Na+ 22.990 + mass Cl- 35.453 + mass WALL 26.9815 + + +Each ``mass`` command assigns a mass in g/mol to an atom type. +The **parameters.inc** file also contains the pair coefficients: + +.. code-block:: lammps + + pair_coeff O O 0.185199 3.1589 + pair_coeff H H 0.0 1.0 + pair_coeff Na+ Na+ 0.04690 2.4299 + pair_coeff Cl- Cl- 0.1500 4.04470 + pair_coeff WALL WALL 11.697 2.574 + pair_coeff O WALL 0.4 2.86645 + +Each ``pair_coeff`` assigns the depth of the LJ potential (in +kcal/mol), and the distance (in Ångströms) at which the +particle-particle potential energy is 0. As noted in previous +tutorials, with the important exception of ``pair_coeff O WALL``, +pairwise interactions were only assigned between atoms of identical +types. By default, LAMMPS calculates the pair coefficients for the +interactions between atoms of different types (i and j) by using +geometric average: :math:`\epsilon_{ij} = (\epsilon_{ii} + \epsilon_{jj})/2`, +:math:`\sigma_{ij} = (\sigma_{ii} + \sigma_{jj})/2`. However, if the default +value of :math:`5.941\,\text{kcal/mol}` was used for :math:`\epsilon_\text{1-5}`, +the solid walls would be extremely hydrophilic, causing the water +molecules to form dense layers. As a comparison, the water-water energy +:math:`\epsilon_\text{1-1}` is only :math:`0.185199\,\text{kcal/mol}`. Therefore, +to make the walls less hydrophilic, the value of +:math:`\epsilon_\text{O-WALL}` was reduced. + +Finally, the **parameters.inc** file contains the following two lines: + +.. code-block:: lammps + + bond_coeff O-H 0 0.9572 + angle_coeff H-O-H 0 104.52 + +The ``bond_coeff`` command, used here for the O-H bond of the water +molecule, sets both the spring constant of the harmonic potential and the +equilibrium bond distance of :math:`0.9572~\text{Å}`. The constant can be 0 for a +rigid water molecule because the SHAKE algorithm will maintain the rigid +structure of the water molecule (see below) :cite:`ryckaert1977numerical, andersen1983rattle`. +Similarly, the ``angle_coeff`` command for the H-O-H angle of the water molecule sets +the force constant of the angular harmonic potential to 0 and the equilibrium +angle to :math:`104.52^\circ`. + +Alongside **parameters.inc**, the **groups.inc** file contains +several ``group`` commands to selects atoms based on their types: + +.. code-block:: lammps + + group H2O type O H + group Na type Na+ + group Cl type Cl- + group ions union Na Cl + group fluid union H2O ions + +The **groups.inc** file also defines the ``walltop`` and ``wallbot`` +groups, which contain the WALL atoms located in the :math:`z > 0` and :math:`z < 0` regions, respectively: + +.. code-block:: lammps + + group wall type WALL + region rtop block INF INF INF INF 0 INF + region rbot block INF INF INF INF INF 0 + group top region rtop + group bot region rbot + group walltop intersect wall top + group wallbot intersect wall bot + +Currently, the fluid density between the two walls is slightly too high. To avoid +excessive pressure, let us add the following lines into **create.lmp** +to delete about :math:`15~\%` of the water molecules: + +.. code-block:: lammps + + delete_atoms random fraction 0.15 yes H2O NULL 482793 mol yes + +To create an image of the system, add the following ``dump`` image +into **create.lmp**: + +.. code-block:: lammps + + dump mydmp all image 200 myimage-*.ppm type type shiny 0.1 box no 0.01 view 90 0 zoom 1.8 + dump_modify mydmp backcolor white acolor O red adiam O 2 acolor H white adiam H 1 & + acolor Na+ blue adiam Na+ 2.5 acolor Cl- cyan adiam Cl- 3 acolor WALL gray adiam WALL 3 + +Finally, add the following lines into **create.lmp**: + +.. code-block:: lammps + + run 0 + + write_data create.data nocoeff + +The ``run 0`` command runs the simulation for 0 steps, which is sufficient for +creating the system and saving its state. The ``write_data`` command +generates a file called **system.data** containing the information required +to restart the simulation from the final configuration produced by this input +file. With the ``nocoeff`` option, the parameters from the force field are +not included in the **.data** file. Run the **create.lmp** file using LAMMPS, +and a file named **create.data** will be created alongside **create.lmp**. + +.. figure:: figures/systemcreation-light.png + :alt: LAMMPS: electrolyte made of water and salt between walls + :class: only-light + +.. figure:: figures/systemcreation-dark.png + :alt: LAMMPS: electrolyte made of water and salt between walls + :class: only-dark + +.. container:: figurelegend + + Figure: Side view of the system. Periodic images are represented in darker colors. + Water molecules are in red and white, :math:`\text{Na}^+` ions in pink, :math:`\text{Cl}^-` + ions in lime, and wall atoms in gray. Note the absence of atomic defect at the + cell boundaries. + +Energy minimization +------------------- + +Let us move the atoms and place them in more energetically favorable positions +before starting the actual molecular dynamics simulation. + +.. admonition:: If you are using LAMMPS-GUI + :class: gui + + Open the **equilibrate.lmp** file that was downloaded alongside + **create.lmp** during the tutorial setup. + +Create a new file, **equilibrate.lmp**, and copy the following into it: + +.. code-block:: lammps + + boundary p p f + units real + atom_style full + bond_style harmonic + angle_style harmonic + pair_style lj/cut/tip4p/long O H O-H H-O-H 0.1546 12.0 + kspace_style pppm/tip4p 1.0e-5 + kspace_modify slab 3.0 + + read_data create.data + + include parameters.inc + include groups.inc + +The only difference from the previous input is that, instead of creating a new +box and new atoms, we open the previously created **create.data** file. + +Now, let us use the SHAKE algorithm to maintain the shape of the +water molecules :cite:`ryckaert1977numerical, andersen1983rattle`. + +.. code-block:: lammps + + fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H kbond 2000 + +Here the SHAKE algorithm applies to the ``O-H`` bond and the ``H-O-H`` angle +of the water molecules. The ``kbond`` keyword specifies the force constant that will be +used to apply a restraint force when used during minimization. This last keyword is important +here, because the spring constants of the rigid water molecules were set +to 0 (see the **parameters.inc** file). + +Let us also create images of the system and control +the printing of thermodynamic outputs by adding the following lines +to **equilibrate.lmp**: + +.. code-block:: lammps + + dump mydmp all image 1 myimage-*.ppm type type shiny 0.1 box no 0.01 view 90 0 zoom 1.8 + dump_modify mydmp backcolor white acolor O red adiam O 2 acolor H white adiam H 1 & + acolor Na+ blue adiam Na+ 2.5 acolor Cl- cyan adiam Cl- 3 acolor WALL gray adiam WALL 3 + + thermo 1 + thermo_style custom step temp etotal press + +Let us perform an energy minization by adding the following lines to **equilibrate.lmp**: + +.. code-block:: lammps + + minimize 1.0e-6 1.0e-6 1000 1000 + reset_timestep 0 + +When running the **equilibrate.lmp** file with LAMMPS, you should observe that the +total energy of the system is initially very high but rapidly decreases. From the generated +images of the system, you will notice that the atoms and molecules are moving to adopt more favorable positions. + +System equilibration +-------------------- + +Let us equilibrate further the entire system by letting both fluid and piston +relax at ambient temperature. Here, the commands are written within the same +**equilibrate.lmp** file, right after the ``reset_timestep`` command. + +Let us update the positions of all the atoms and use a Nosé-Hoover +thermostat. Add the following lines to **equilibrate.lmp**: + +.. code-block:: lammps + + fix mynvt all nvt temp 300 300 100 + fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H + fix myrct all recenter NULL NULL 0 + timestep 1.0 + +As mentioned previously, the ``fix recenter`` does not influence the dynamics, +but will keep the system in the center of the box, which makes the +visualization easier. Then, add the following lines into **equilibrate.lmp** +for the trajectory visualization: + +.. code-block:: lammps + + undump mydmp + dump mydmp all image 250 myimage-*.ppm type type shiny 0.1 box no 0.01 view 90 0 zoom 1.8 + dump_modify mydmp backcolor white acolor O red adiam O 2 acolor H white adiam H 1 & + acolor Na+ blue adiam Na+ 2.5 acolor Cl- cyan adiam Cl- 3 acolor WALL gray adiam WALL 3 + +The ``undump`` command is used to cancel the previous ``dump`` command. +Then, a new ``dump`` command with a larger dumping period is used. + +To monitor the system equilibration, let us print the distance between +the two walls. Add the following lines to **equilibrate.lmp**: + +.. code-block:: lammps + + variable walltopz equal xcm(walltop,z) + variable wallbotz equal xcm(wallbot,z) + variable deltaz equal v_walltopz-v_wallbotz + + thermo 250 + thermo_style custom step temp etotal press v_deltaz + +The first two variables extract the centers of mass of the two walls. The +``deltaz`` variable is then used to calculate the difference between the two +variables ``walltopz`` and ``wallbotz``, i.e. the distance between the +two centers of mass of the walls. + +Finally, let us run the simulation for 30 ps by adding a ``run`` command +to **equilibrate.lmp**: + +.. code-block:: lammps + + run 30000 + + write_data equilibrate.data nocoeff + +Run the **equilibrate.lmp** file using LAMMPS. Both the pressure and the distance +between the two walls show oscillations at the start of the simulation +but eventually stabilize at their equilibrium values toward +the end of the simulation. + +.. admonition:: Note + :class: non-title-info + + Note that it is generally recommended to run a longer equilibration. In this case, + the slowest process in the system is likely ionic diffusion. + Therefore, the equilibration period should, in principle, exceed the time required + for the ions to diffuse across the size of the pore, i.e. :math:`H_\text{pore}^2/D_\text{ions}`. + Using :math:`H_\text{pore} \approx 1.2~\text{nm}` as the final pore size + and :math:`D_\text{ions} \approx 1.5 \cdot 10^{-9}~\text{m}^2/\text{s}` + as the typical diffusion coefficient for sodium chloride in water at room + temperature :cite:`mills1955remeasurement`, one finds that the equilibration + should be on the order of one nanosecond. + +.. figure:: figures/NANOSHEAR-equilibration-dm.png + :class: only-dark + :alt: Evolution of the pressure and distance for the elecrolyte + +.. figure:: figures/NANOSHEAR-equilibration.png + :class: only-light + :alt: Evolution of the pressure and distance for the elecrolyte + +.. container:: figurelegend + + Figure: a) Pressure, :math:`p`, of the nanosheared electrolyte system as a function + of the time, :math:`t`. b) Distance between the walls, :math:`\Delta z`, as a + function of :math:`t`. + +Imposed shearing +---------------- + +From the equilibrated configuration, let us impose a lateral motion on the two +walls and shear the electrolyte. + +.. admonition:: If you are using LAMMPS-GUI + :class: gui + + Open the last input file named **shearing.lmp**. + +Create a new file, **shearing.lmp**, and copy the following into it: + +.. code-block:: lammps + + boundary p p f + units real + atom_style full + bond_style harmonic + angle_style harmonic + pair_style lj/cut/tip4p/long O H O-H H-O-H 0.1546 12.0 + kspace_style pppm/tip4p 1.0e-5 + kspace_modify slab 3.0 + + read_data equilibrate.data + + include parameters.inc + include groups.inc + +To address the dynamics of the system, add the following lines to +**shearing.lmp**: + +.. code-block:: lammps + + compute Tfluid fluid temp/partial 0 1 1 + fix mynvt1 fluid nvt temp 300 300 100 + fix_modify mynvt1 temp Tfluid + + compute Twall wall temp/partial 0 1 1 + fix mynvt2 wall nvt temp 300 300 100 + fix_modify mynvt2 temp Twall + + fix myshk H2O shake 1.0e-5 200 0 b O-H a H-O-H + fix myrct all recenter NULL NULL 0 + timestep 1.0 + +One key difference with the previous input is that, here, two thermostats are used, +one for the fluid (``mynvt1``) and one for the solid (``mynvt2``). +The combination of ``fix_modify`` with ``compute temp`` ensures +that the correct temperature values are used by the thermostats. Using +``compute`` commands for the temperature with ``temp/partial 0 1 1`` is +intended to exclude the :math:`x` coordinate from the thermalization, which is important since a +large velocity will be imposed along the :math:`x` direction. + +Then, let us impose the velocity of the two walls by adding the following +commands to **shearing.lmp**: + +.. code-block:: lammps + + fix mysf1 walltop setforce 0 NULL NULL + fix mysf2 wallbot setforce 0 NULL NULL + velocity wallbot set -2e-4 NULL NULL + velocity walltop set 2e-4 NULL NULL + +The ``setforce`` commands cancel the forces on ``walltop`` and +``wallbot``. As a result, the atoms in these two groups will not +experience any forces from the rest of the system. Consequently, in the absence of +external forces, these atoms will conserve the initial velocities imposed by the +two ``velocity`` commands. + +Finally, let us generate images of the systems and print the values of the +forces exerted by the fluid on the walls, as given by ``f_mysf1[1]`` +and ``f_mysf2[1]``. Add these lines to **shearing.lmp**: + +.. code-block:: lammps + + dump mydmp all image 250 myimage-*.ppm type type shiny 0.1 box no 0.01 view 90 0 zoom 1.8 + dump_modify mydmp backcolor white acolor O red adiam O 2 acolor H white adiam H 1 & + acolor Na+ blue adiam Na+ 2.5 acolor Cl- cyan adiam Cl- 3 acolor WALL gray adiam WALL 3 + + thermo 250 + thermo_modify temp Tfluid + thermo_style custom step temp etotal f_mysf1[1] f_mysf2[1] + +Let us also extract the density and velocity profiles using +the ``chunk/atom`` and ``ave/chunk`` commands. These commands are +used to divide the system into bins and return the desired quantities, here the velocity +along :math:`x` (``vx``) within the bins. Add the following lines to **shearing.lmp**: + +.. code-block:: lammps + + compute cc1 H2O chunk/atom bin/1d z 0.0 0.25 + compute cc2 wall chunk/atom bin/1d z 0.0 0.25 + compute cc3 ions chunk/atom bin/1d z 0.0 0.25 + + fix myac1 H2O ave/chunk 10 15000 200000 & + cc1 density/mass vx file shearing-water.dat + fix myac2 wall ave/chunk 10 15000 200000 & + cc2 density/mass vx file shearing-wall.dat + fix myac3 ions ave/chunk 10 15000 200000 & + cc3 density/mass vx file shearing-ions.dat + + run 200000 + +Here, a bin size of :math:`0.25\,\text{Å}` is used for the density +profiles generated by the ``ave/chunk`` commands, and three +**.dat** files are created for the water, the walls, and the ions, +respectively. With values of ``10 15000 200000``, the velocity +``vx`` will be evaluated every 10 steps during the final 150,000 +steps of the simulations. The result will be averaged and printed only +once at the 200,000 th step. + +Run the simulation using LAMMPS. The averaged velocity +profile for the fluid is plotted below. +As expected for such Couette flow geometry, the fluid velocity increases +linearly along :math:`z`, and is equal to the walls velocities at the fluid-solid +interfaces (no-slip boundary conditions). + +.. figure:: figures/NANOSHEAR-profiles-dm.png + :class: only-dark + :alt: Velocity profiles for the elecrolyte + +.. figure:: figures/NANOSHEAR-profiles.png + :class: only-light + :alt: Velocity profiles for the elecrolyte + +.. container:: figurelegend + + Figure: Velocity profiles for water (blue) and walls (orange) along the :math:`z`-axis. + +From the force applied by the fluid on the solid, one can extract the stress +within the fluid, which enables the measurement of its viscosity :math:`\eta` +according to + +.. math:: + :label: eq_eta + + \eta = \tau / \dot{\gamma} + +where :math:`\tau` is the stress applied by +the fluid on the shearing wall, and :math:`\dot{\gamma}` the shear rate +:cite:`gravelle2021violations`. Here, the shear rate is +approximately :math:`\dot{\gamma} = 20 \cdot 10^9\,\text{s}^{-1}`, +the average force on each wall is given by ``f_mysf1[1]`` and ``f_mysf2[1]`` +and is approximately :math:`2.7\,\mathrm{kcal/mol/Å}` in magnitude. Using a surface area +for the walls of :math:`A = 6 \cdot 10^{-18}\,\text{m}^2`, one obtains an estimate for +the shear viscosity for the confined fluid of :math:`\eta = 3.1\,\text{mPa.s}` using Eq. :eq:`eq_eta`. + +.. admonition:: Note + :class: non-title-info + + The viscosity calculated at such a high shear rate may differ from the expected + *bulk* value. In general, it is recommended to use a lower value for the + shear rate. Note that for lower shear rates, the ratio of noise-to-signal is + larger, and longer simulations are needed. Another important point to consider + is that the viscosity of a fluid next to a solid surface is typically larger + than in bulk due to interaction with the walls :cite:`wolde-kidanInterplayInterfacialViscosity2021`. + Therefore, one expects the present simulation to yield a viscosity that is slightly + higher than what would be measured in the absence of walls. + diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif.png b/docs/sphinx/source/tutorial5/avatars/SiO_gif.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif.png rename to docs/sphinx/source/tutorial5/avatars/SiO_gif.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_dark.png b/docs/sphinx/source/tutorial5/avatars/SiO_gif_dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_dark.png rename to docs/sphinx/source/tutorial5/avatars/SiO_gif_dark.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_dark.webp b/docs/sphinx/source/tutorial5/avatars/SiO_gif_dark.webp similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_dark.webp rename to docs/sphinx/source/tutorial5/avatars/SiO_gif_dark.webp diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_light.png b/docs/sphinx/source/tutorial5/avatars/SiO_gif_light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_light.png rename to docs/sphinx/source/tutorial5/avatars/SiO_gif_light.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_light.webp b/docs/sphinx/source/tutorial5/avatars/SiO_gif_light.webp similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/SiO_gif_light.webp rename to docs/sphinx/source/tutorial5/avatars/SiO_gif_light.webp diff --git a/docs/sphinx/source/tutorial5/exercises.rst b/docs/sphinx/source/tutorial5/exercises.rst new file mode 100644 index 000000000..215f4b189 --- /dev/null +++ b/docs/sphinx/source/tutorial5/exercises.rst @@ -0,0 +1,41 @@ +Going further with exercises +============================ + +Hydrate the structure +--------------------- + +Add water molecules to the current structure, and follow the reactions over +time. + +.. figure:: figures/hydrated-light.png + :alt: Cracked silicon oxide after addition of water molecule + :class: only-light + +.. figure:: figures/hydrated-dark.png + :alt: Cracked silicon oxide after addition of water molecule + :class: only-dark + +.. container:: figurelegend + + Figure: Cracked silicon oxide after the addition of water + molecules. The atoms are colored by their charges. + +A slightly acidic bulk solution +------------------------------- + +Create a bulk water system with a few hydronium ions (:math:`H_3O^+` +or :math:`H^+`) using ReaxFF. The addition of hydronium ions will make the +system acidic. + +.. figure:: figures/acidic-water-light.png + :alt: Acidic bulk water with ReaxFF + :class: only-light + +.. figure:: figures/acidic-water-dark.png + :alt: Acidic bulk water with ReaxFF + :class: only-dark + +.. container:: figurelegend + + Figure: Slightly acidic bulk water simulated with ReaxFF. 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docs/sphinx/source/tutorial5/figures/decorated-light.png diff --git a/docs/sphinx/source/tutorial5/figures/deform.ipynb b/docs/sphinx/source/tutorial5/figures/deform.ipynb new file mode 100644 index 000000000..4f0cbff66 --- /dev/null +++ b/docs/sphinx/source/tutorial5/figures/deform.ipynb @@ -0,0 +1,261 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "faf6001c", + "metadata": {}, + "outputs": [], + "source": [ + "def sliding_average(data, window_size):\n", + " \"\"\"Calculate the sliding (moving) average of a dataset with edge handling.\"\"\"\n", + " pad_width = window_size // 2\n", + " padded_data = np.pad(data, pad_width, mode='edge')\n", + " smoothed_data = np.convolve(padded_data, np.ones(window_size) / window_size, mode='valid')\n", + " return smoothed_data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "23cc2c26", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"SIO-distribution\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + " \n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,9), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=2)\n", + "\n", + " hist_O = np.loadtxt(path_data + \"relax-O.histo\", skiprows=4)\n", + " hist_Si = np.loadtxt(path_data + \"relax-Si.histo\", skiprows=4)\n", + "\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = hist_Si[:,1][hist_Si[:,3]>0], y = hist_Si[:,3][hist_Si[:,3]>0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.add_plot(x = hist_O[:,1][hist_O[:,3]>0], y = hist_O[:,3][hist_O[:,3]>0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$P (q)$',\n", + " xlabel =None, xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-1., 2.4, 0.5), y_ticks=np.arange(0, 0.031, 0.01),\n", + " x_boundaries=(-1.3, 2.3), y_boundaries=(-0.001, 0.036))\n", + "\n", + " hist_O = np.loadtxt(path_data + \"deform-O.histo\", skiprows=5009)\n", + " hist_Si = np.loadtxt(path_data + \"deform-Si.histo\", skiprows=5009)\n", + "\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = hist_Si[:,1][hist_Si[:,3]>0], y = hist_Si[:,3][hist_Si[:,3]>0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.add_plot(x = hist_O[:,1][hist_O[:,3]>0], y = hist_O[:,3][hist_O[:,3]>0], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$P (q)$',\n", + " xlabel = r'$q ~ (\\mathrm{e})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-1., 2.4, 0.5), y_ticks=np.arange(0, 0.031, 0.01),\n", + " x_boundaries=(-1.3, 2.3), y_boundaries=(-0.001, 0.036))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "7d95076b", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(\"deform.log\")\n", + "timestep = 0.5 # fs\n", + "time = log.get(\"Step\", run_num=0)*timestep/1000 # ps\n", + "Volume = log.get(\"Volume\", run_num=0)/1000 # nm3\n", + "qSi = log.get(\"v_qSi\", run_num=0)\n", + "qO = log.get(\"v_qO\", run_num=0)\n", + "Temp = log.get(\"Temp\", run_num=0)\n", + "\n", + "time1 = log.get(\"Step\", run_num=1)*timestep/1000 # ps\n", + "Volume1 = log.get(\"Volume\", run_num=1)/1000 # nm3\n", + "qSi1 = log.get(\"v_qSi\", run_num=1)\n", + "qO1 = log.get(\"v_qO\", run_num=1)\n", + "Temp1 = log.get(\"Temp\", run_num=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "7daa6044", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"SIO-deformed-charge\"\n", + "\n", + "# Initialise figure\n", + "myplt = PltTools()\n", + "myplt.prepare_figure(fig_size = (12,8), dark_mode = False,\n", + " transparency = False, use_serif=False, n_colone=1, n_line=2)\n", + "# Panel a\n", + "myplt.add_panel()\n", + "myplt.add_plot(x = time, y = qSi, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + "myplt.add_plot(x = time1, y = qSi1, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + "myplt.complete_panel(ylabel = r'$q_\\mathrm{Si} ~ (\\mathrm{e})$',\n", + " xlabel = None, xpad = 10, legend=True, handlelength_legend=1)\n", + "myplt.set_boundaries(x_ticks=np.arange(0, 15.1, 2), y_ticks=np.arange(1.4, 2.08, 0.2),\n", + " x_boundaries=(-0.1, 15.1), y_boundaries=(1.4, 2.05))\n", + "\n", + "# Panel b\n", + "myplt.add_panel()\n", + "\n", + "myplt.add_plot(x = time, y = Temp, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + "myplt.add_plot(x = time1, y = Temp1, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + "\n", + "\"\"\"\n", + "timeA = sliding_average(time, 3)\n", + "TempA = sliding_average(Temp, 3)\n", + "time1A = sliding_average(time1, 3)\n", + "Temp1A = sliding_average(Temp1, 3)\n", + "myplt.add_plot(x = timeA, y = TempA, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + "myplt.add_plot(x = time1A, y = Temp1A, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + "\"\"\"\n", + "\n", + "myplt.complete_panel(ylabel = r'$T ~ (\\mathrm{K})$',\n", + " xlabel = r'$t ~ (\\mathrm{ps})$', xpad = 10, legend=True, handlelength_legend=1)\n", + "myplt.set_boundaries(x_ticks=np.arange(0, 15.1, 2), y_ticks=np.arange(300, 801, 100),\n", + " x_boundaries=(-0.1, 15.1), y_boundaries=(260, 800))\n", + "\n", + "# Print figure\n", + "myplt.add_subplotlabels()\n", + "myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/deformed-charge-article.png b/docs/sphinx/source/tutorial5/figures/deformed-charge-article.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/deformed-charge-article.png rename to 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rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/hydronium_transfert_light.webp rename to docs/sphinx/source/tutorial5/figures/hydronium_transfert_light.webp diff --git a/docs/sphinx/source/tutorial5/figures/relax.ipynb b/docs/sphinx/source/tutorial5/figures/relax.ipynb new file mode 100644 index 000000000..a958d566f --- /dev/null +++ b/docs/sphinx/source/tutorial5/figures/relax.ipynb @@ -0,0 +1,164 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "509b0639", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"relax.log\")\n", + "timestep = 0.5 # fs\n", + "time = log.get(\"Step\", run_num=0)*timestep/1000 # ps\n", + "Volume = log.get(\"Volume\", run_num=0)/1000 # nm3\n", + "qSi = log.get(\"v_qSi\", run_num=0)\n", + "qO = log.get(\"v_qO\", run_num=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "52e1026b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"SIO-charge\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,9), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = qSi, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$q_\\mathrm{Si} ~ (\\mathrm{e})$',\n", + " xlabel = None, xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 2.6, 0.5), y_ticks=np.arange(1.75, 1.91, 0.05),\n", + " x_boundaries=(-0.1, 2.6), y_boundaries=(1.74, 1.91))\n", + "\n", + " # Panel b\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = Volume, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$V ~ (\\mathrm{nm^3})$',\n", + " xlabel = r'$t ~ (\\mathrm{ps})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 2.6, 0.5), y_ticks=np.arange(7.2, 8.41, 0.4),\n", + " x_boundaries=(-0.1, 2.6), y_boundaries=(7.2, 8.4))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/silicon-dark.png b/docs/sphinx/source/tutorial5/figures/silicon-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/silicon-dark.png rename to docs/sphinx/source/tutorial5/figures/silicon-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/silicon-light.png b/docs/sphinx/source/tutorial5/figures/silicon-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/silicon-light.png rename to docs/sphinx/source/tutorial5/figures/silicon-light.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/temperature-article.png b/docs/sphinx/source/tutorial5/figures/temperature-article.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/temperature-article.png rename to docs/sphinx/source/tutorial5/figures/temperature-article.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-article.png b/docs/sphinx/source/tutorial5/figures/volume-article.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-article.png rename to docs/sphinx/source/tutorial5/figures/volume-article.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-dark.png b/docs/sphinx/source/tutorial5/figures/volume-dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-dark.png rename to docs/sphinx/source/tutorial5/figures/volume-dark.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-dm.png b/docs/sphinx/source/tutorial5/figures/volume-dm.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-dm.png rename to docs/sphinx/source/tutorial5/figures/volume-dm.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-light.png b/docs/sphinx/source/tutorial5/figures/volume-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume-light.png rename to docs/sphinx/source/tutorial5/figures/volume-light.png diff --git a/docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume.png b/docs/sphinx/source/tutorial5/figures/volume.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/reactive-silicon-dioxide/volume.png rename to docs/sphinx/source/tutorial5/figures/volume.png diff --git a/docs/sphinx/source/tutorial5/introduction.rst b/docs/sphinx/source/tutorial5/introduction.rst new file mode 100644 index 000000000..7ca884416 --- /dev/null +++ b/docs/sphinx/source/tutorial5/introduction.rst @@ -0,0 +1,21 @@ +.. figure:: avatars/SiO_gif_light.webp + :height: 250 + :alt: Figure showing silicon dioxide structure with colored charges as simulated with lammps and reaxff + :class: only-light + :align: right + +.. figure:: avatars/SiO_gif_dark.webp + :height: 250 + :alt: Figure showing silicon dioxide structure with colored charges as simulated with lammps and reaxff + :class: only-dark + :align: right + + +The objective of this tutorial is to demonstrate how the reactive force field ReaxFF +can be used to calculate the partial charges of a system undergoing deformation, as well as +the formation and breaking of chemical bonds :cite:`van2001reaxff, zou2012investigation`. +The system simulated in this tutorial is a block of silicon dioxide :math:`\text{SiO}_2` +which is deformed until it ruptures. Particular attention is given to the evolution +of atomic charges during deformation, with a focus on tracking chemical reactions +resulting from the deformation over time. + diff --git a/docs/sphinx/source/tutorial5/reactive-silicon-dioxide.rst b/docs/sphinx/source/tutorial5/reactive-silicon-dioxide.rst new file mode 100644 index 000000000..9e5358230 --- /dev/null +++ b/docs/sphinx/source/tutorial5/reactive-silicon-dioxide.rst @@ -0,0 +1,16 @@ +.. _reactive-silicon-dioxide-label: + +Reactive silicon dioxide +************************ + +.. container:: hatnote + + Simulating a chemically reactive structure + +.. include:: introduction.rst +.. include:: ../shared/recommend-tutorial1.rst +.. include:: ../shared/cite.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst diff --git a/docs/sphinx/source/tutorial5/tutorial.rst b/docs/sphinx/source/tutorial5/tutorial.rst new file mode 100644 index 000000000..7bc5e57e1 --- /dev/null +++ b/docs/sphinx/source/tutorial5/tutorial.rst @@ -0,0 +1,422 @@ +Prepare and relax +================= + +The first step is to relax the structure with ReaxFF, which which will be achieved using +molecular dynamics. To ensure the system equilibrates properly, we will monitor certain +parameters over time, such as the system volume. + +Create a folder if needed and +place the initial input file, **relax.lmp**, into it. Then, open the +file in a text editor of your choice, and copy the following into it: + +.. code-block:: lammps + + units real + atom_style full + + read_data silica.data + +.. admonition:: If you are using LAMMPS-GUI + :class: gui + + To begin this tutorial, select ``Start Tutorial 5`` from the + ``Tutorials`` menu of LAMMPS--GUI and follow the instructions. + The editor should display the following content corresponding to **relax.lmp** + +So far, the input is very similar to what was seen in the previous tutorials. +Some basic parameters are defined (``units`` and ``atom_style``), +and a **.data** file is imported by the ``read_data`` command. + +The initial topology given by |silica_data_5| +is a small amorphous silica structure. This structure was created using a force field called +Vashishta :cite:`vashishta1990interaction`. If you open the **silica.data** +file, you will find in the ``Atoms`` section that all silicon atoms have a +charge of :math:`q = 1.1\,\text{e}`, and all oxygen atoms have a charge of :math:`q = -0.55\,\text{e}`. + +.. |silica_data_5| raw:: html + + silica.data + +.. admonition:: Note + :class: non-title-info + + Assigning the same charge to all atoms of the same type is common with many + force fields, including the force fields used in the previous tutorials. This + changes once ReaxFF is used: the charge of each atom will adjust to its local + environment. + +Next, copy the following three crucial lines into the **relax.lmp** file: + +.. code-block:: lammps + + pair_style reaxff NULL safezone 3.0 mincap 150 + pair_coeff * * reaxCHOFe.inc Si O + fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400 + +In this case, the ``pair_style reaxff`` is used without a control file. The +``safezone`` and ``mincap`` keywords are added to prevent +allocation issues, which sometimes can trigger segmentation faults and +``bondchk`` errors. The ``pair_coeff`` command uses the |reaxCHOFe_inc_5| +file, which should have been downloaded during the tutorial set up. Finally, the +``fix qeq/reaxff`` is used to perform charge equilibration :cite:`rappe1991charge`, +which occurs at every step. The values 0.0 and 10.0 represent the +low and the high cutoffs, respectively, and :math:`1.0 \text{e} -6` is the tolerance. +The ``maxiter`` sets an upper limit to the number of attempts to +equilibrate the charge. + +.. |reaxCHOFe_inc_5| raw:: html + + reaxCHOFe.inc + +Next, add the following commands to the **relax.lmp** file to track the +evolution of the charges during the simulation: + +.. code-block:: lammps + + group grpSi type Si + group grpO type O + variable qSi equal charge(grpSi)/count(grpSi) + variable qO equal charge(grpO)/count(grpO) + variable vq atom q + +To print the averaged charges ``qSi`` and ``qO`` using the +``thermo_style`` command, and create images of the system. Add the +following lines to **relax.lmp**: + +.. code-block:: lammps + + thermo 100 + thermo_style custom step temp etotal press vol v_qSi v_qO + dump viz all image 100 myimage-*.ppm q type shiny 0.1 box no 0.01 view 180 90 zoom 2.3 size 1200 500 + dump_modify viz adiam Si 2.6 adiam O 2.3 backcolor white amap -1 2 ca 0.0 3 min royalblue 0 green max orangered + +Here, the atoms are colored by their charges ``q``, ranging from royal blue +(when :math:`q=-1\,\text{e}`) to orange-red (when :math:`q=2\,\text{e}`). + +.. figure:: figures/silicon-light.png + :alt: Amorphous silica colored by charges using VMD + :class: only-light + +.. figure:: figures/silicon-dark.png + :alt: Amorphous silica colored by charges using VMD + :class: only-dark + +.. container:: figurelegend + + Figure: Amorphous silicon oxide. The atoms are colored by their + charges. Dangling oxygen groups appear in greenish, bulk Si atoms with a charge of + about :math:`1.8~\text{e}` appear in red/orange, and bulk O atoms with a charge of + about :math:`-0.9 ~ \text{e}` appear in blue. + +We can generate histograms of the charges for each atom type using +``fix ave/histo`` commands: + +.. code-block:: lammps + + fix myhis1 grpSi ave/histo 10 500 5000 -1.5 2.5 1000 v_vq file relax-Si.histo mode vector + fix myhis2 grpO ave/histo 10 500 5000 -1.5 2.5 1000 v_vq file relax-O.histo mode vector + +We can also use the ``fix reaxff/species`` to evaluate what species are +present within the simulation. It will be useful later when the system is deformed, +and bonds are broken: + +.. code-block:: lammps + + fix myspec all reaxff/species 5 1 5 relax.species element Si O + +Here, the information will be printed every 5 steps in a file called **relax.species**. +Let us perform a very short run using the anisotropic NPT command and relax the +density of the system: + +.. code-block:: lammps + + velocity all create 300.0 32028 + fix mynpt all npt temp 300.0 300.0 100 aniso 1.0 1.0 1000 + timestep 0.5 + + run 5000 + + write_data relax.data + +Run the **relax.lmp** file using LAMMPS. As seen from **relax.species**, +only one species is detected, called ``O384Si192``, representing the entire system. + +As the simulation progresses, the charge of every atom fluctuates +because it is adjusting to the local environment of the atom. +It is also observed that the averaged charges for silicon and oxygen +atoms fluctuate significantly at the beginning of the simulation, corresponding +to a rapid change in the system volume, which causes interatomic distances to +shift quickly. The atoms with the +most extreme charges are located at structural defects, +such as dangling oxygen groups. + +.. figure:: figures/SIO-charge-dm.png + :class: only-dark + :alt: Average charge per atom of the silicon + +.. figure:: figures/SIO-charge.png + :class: only-light + :alt: Average charge per atom of the silicon + +.. container:: figurelegend + + Figure: a) Average charge per atom of the silicon, :math:`q_\text{Si}`, atoms as + a function of time, :math:`t`, during equilibration of the :math:`\text{SiO}_2` + system. b) Volume of the system, :math:`V`, as a function of :math:`t`. + +.. figure:: figures/silicon-light.png + :alt: Amorphous silica colored by charges using VMD + :class: only-light + +.. figure:: figures/silicon-dark.png + :alt: Amorphous silica colored by charges using VMD + :class: only-dark + +.. container:: figurelegend + + Figure: A slice of the amorphous silica, where atoms are colored by their charges. + Dangling oxygen groups appear in greenish, bulk Si atoms with a charge of about + :math:`1.8~\text{e}` appear in red/orange, and bulk O atoms with a charge of about + :math:`-0.9~\text{e}` appear in blue. + +Finally, the generated **.histo** files can be used to +plot the probability distributions, :math:`P(q)`. + +.. figure:: figures/SIO-distribution-dm.png + :class: only-dark + :alt: Average charge per atom of the silicon + +.. figure:: figures/SIO-distribution.png + :class: only-light + :alt: Average charge per atom of the silicon + +.. container:: figurelegend + + Figure: a) Probability distributions of charge of silicon (positive, blue) and oxygen + (negative, orange) atoms during the equilibration of the :math:`\text{SiO}_2` + system. b) Same probability distributions as in panel (a) after the deformation. + +Deform the structure +-------------------- + +Let us apply a deformation to the structure to force some :math:`\text{Si}-\text{O}` +bonds to break (and eventually re-assemble). Open the **deform.lmp** +file, which must contain the following lines: + +.. code-block:: lammps + + units real + atom_style full + + read_data relax.data + + pair_style reaxff NULL safezone 3.0 mincap 150 + pair_coeff * * reaxCHOFe.inc Si O + fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400 + + group grpSi type Si + group grpO type O + variable qSi equal charge(grpSi)/count(grpSi) + variable qO equal charge(grpO)/count(grpO) + variable vq atom q + + thermo 200 + thermo_style custom step temp etotal press vol v_qSi v_qO + dump viz all image 100 myimage-*.ppm q type shiny 0.1 box no 0.01 view 180 90 zoom 2.3 size 1200 500 + dump_modify viz adiam Si 2.6 adiam O 2.3 backcolor white amap -1 2 ca 0.0 3 min royalblue 0 green max orangered + + fix myhis1 grpSi ave/histo 10 500 5000 -1.5 2.5 1000 v_vq file deform-Si.histo mode vector + fix myhis2 grpO ave/histo 10 500 5000 -1.5 2.5 1000 v_vq file deform-O.histo mode vector + fix myspec all reaxff/species 5 1 5 deform.species element Si O + +The only difference with the previous **relax.lmp** file is the path to +the **relax.data** file. + +Next, let us use ``fix nvt`` instead of ``fix npt`` to apply a +Nosé-Hoover thermostat without a barostat: + +.. code-block:: lammps + + fix mynvt all nvt temp 300.0 300.0 100 + timestep 0.5 + +Here, no barostat is used because the change in the box volume will be imposed +by the ``fix deform``. + +Let us run for 5000 steps without deformation, then apply the ``fix deform`` +to progressively elongate the box along the :math:`x`-axis during 25000 steps. Add +the following line to **deform.lmp**: + +.. code-block:: lammps + + run 5000 + + fix mydef all deform 1 x erate 5e-5 + + run 25000 + + write_data deform.data + +Run the **deform.lmp** file using LAMMPS. During the deformation, the charge +values progressively evolve until the structure eventually breaks down. After the +structure breaks down, the charges equilibrate near new average values that differ +from the initial averages. The difference +between the initial and the final charges can be explained by the presence of +defects, as well as new solid/vacuum interfaces, and the fact that surface atoms +typically have different charges compared to bulk atoms. +You can also see a sharp increase in temperature during the rupture of +the material. + +.. figure:: figures/deformed-charge-dm.png + :class: only-dark + :alt: Evolution of the pressure and distance for the elecrolyte + +.. figure:: figures/deformed-charge.png + :class: only-light + :alt: Evolution of the pressure and distance for the elecrolyte + +.. container:: figurelegend + + Figure: a) Average charge per atom of the silicon, :math:`q_\text{Si}`, atoms as + a function of time, :math:`t`, during deformation of the :math:`\text{SiO}_2` system. + The break down of the + silica structure occurs near :math:`t = 11` ps. b) Temperature, :math:`T`, of the + system as a function of :math:`t`. + +You can examine the charge distribution after deformation, as well as during +deformation. As expected, the final +charge distribution slightly differs from the previously calculated one. If +no new species were formed during the simulation, the **deform.species** file +should look like this: + +.. code-block:: lammps + + # Timestep No_Moles No_Specs O384Si192 + 5 1 1 1 + (...) + # Timestep No_Moles No_Specs O384Si192 + 30000 1 1 1 + +Sometimes, :math:`\text{O}_2` molecules are formed during the deformation. If this occurs, +a new column ``O2`` appears in the **deform.species** file. + +Decorate the surface +-------------------- + +Under ambient conditions, some of the surface :math:`\text{SiO}_2` atoms become chemically +passivated by forming covalent bonds with hydrogen (H) atoms :cite:`sulpizi2012silica`. +We will add hydrogen atoms randomly to the cracked silica and observe how the +system evolves. To do so, we first need to modify the previously generated data +file **deform.data** and make space for a third atom type. +Copy **deform.data**, name the copy **deform-mod.data**, and modify the +first lines of **deform-mod.data** as follows: + +.. code-block:: lammps + + 576 atoms + 3 atom types + + (...) + + Atom Type Labels + + 1 Si + 2 O + 3 H + + Masses + + Si 28.0855 + O 15.999 + H 1.008 + + (...) + +Open the **decorate.lmp** file, which must contain the following lines: + +.. code-block:: lammps + + units real + atom_style full + + read_data deform-mod.data + displace_atoms all move -12 0 0 # optional + + pair_style reaxff NULL safezone 3.0 mincap 150 + pair_coeff * * reaxCHOFe.inc Si O H + fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400 + +The ``displace_atoms`` command is used to move the center of the +crack near the center of the box. This step is optional but makes for a nicer +visualization. A different value for the shift may be needed in +your case, depending on the location of the crack. A difference with the previous +input is that three atom types are specified in the ``pair_coeff`` command, i.e. +``Si O H``. + +Then, let us adapt some familiar commands to measure the charges of all three +types of atoms, and output the charge values into log files: + +.. code-block:: lammps + + group grpSi type Si + group grpO type O + group grpH type H + variable qSi equal charge(grpSi)/count(grpSi) + variable qO equal charge(grpO)/count(grpO) + variable qH equal charge(grpH)/(count(grpH)+1e-10) + + thermo 5 + thermo_style custom step temp etotal press v_qSi v_qO v_qH + + dump viz all image 100 myimage-*.ppm q type shiny 0.1 box no 0.01 view 180 90 zoom 2.3 size 1200 500 + dump_modify viz adiam Si 2.6 adiam O 2.3 adiam H 1.0 backcolor white amap -1 2 ca 0.0 3 min royalblue 0 green max orangered + + fix myspec all reaxff/species 5 1 5 decorate.species element Si O H + +Here, the :math:`+1 \mathrm{e}{-10}` was added to the denominator of the ``variable qH`` +to avoid dividing by 0 at the beginning of the simulation. Finally, let us +create a loop with 10 steps, and create two hydrogen atoms at random locations at +every step: + +.. code-block:: lammps + + fix mynvt all nvt temp 300.0 300.0 100 + timestep 0.5 + + label loop + variable a loop 10 + + variable seed equal 35672+${a} + create_atoms 3 random 2 ${seed} NULL overlap 2.6 maxtry 50 + + run 2000 + + next a + jump SELF loop + +Run the simulation with LAMMPS. When the simulation is over, +it can be seen from the **decorate.species** file that +all the created hydrogen atoms reacted with the :math:`\text{SiO}_{2}` structure to +form surface groups (such as hydroxyl (-OH) groups). + +.. code-block:: lammps + + (...) + # Timestep No_Moles No_Specs H20O384Si192 + 20000 1 1 1 + +At the end of the simulation, hydroxyl (-OH) groups can be seen at the interfaces. + +.. figure:: figures/decorated-dark.png + :class: only-dark + :alt: Cracked silicon oxide after the addition of hydrogen atoms simulated using LAMMPS molecular dynamics + +.. figure:: figures/decorated-light.png + :class: only-light + :alt: Cracked silicon oxide after the addition of hydrogen atoms simulated using LAMMPS molecular dynamics + +.. container:: figurelegend + + Figure: Cracked silicon oxide after the addition of hydrogen atoms. + The atoms are colored by their charges, with the newly added hydrogen atoms appearing as small + greenish spheres. \ No newline at end of file diff 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The parameters for the +:math:`\text{CO}_2` +molecule are the following: + +.. code-block:: lammps + + pair_coeff 5 5 lj/cut/tip4p/long 0.0179 2.625854 + pair_coeff 6 6 lj/cut/tip4p/long 0.0106 2.8114421 + bond_coeff 2 46.121 1.17 + angle_coeff 2 2.0918 180 + +The atom of type 5 is an oxygen of +mass 15.9994, and the atom of type 6 is a carbon of mass 12.011. + +.. figure:: figures/H2O-CO2-dark.png + :alt: silica block adsorbed water and CO2 + :class: only-dark + +.. figure:: figures/H2O-CO2-light.png + :alt: silica block adsorbed water and CO2 + :class: only-light + +.. container:: figurelegend + + Figure: Cracked silica with adsorbed water and :math:`\text{CO}_2` molecules (in green). + +.. |CO2-template| raw:: html + + CO2 template + +Adsorb water in ZIF-8 nanopores +------------------------------- + +.. figure:: figures/zif8-dark.png + :alt: zif-8 with water + :class: only-dark + :height: 250 + :align: right + +.. figure:: figures/zif8-light.png + :alt: zif-8 with water + :class: only-light + :height: 250 + :align: right + +Use the same protocol as the one implemented in this tutorial to add water +molecules to a Zif-8 nanoporous material. A snapshot of the system with a +few water molecules is shown on the right. + +Download the initial Zif-8 |Zif-8-structure|, +the |Zif-8-parameters| file, and this +new |water-template|. The ZIF-8 structure is made +of 7 atom types (C1, C2, C3, H2, H3, N, Zn), connected +by bonds, angles, dihedrals, and impropers. It uses the +same *pair_style* as water, +so there is no need to use *hybrid pair_style*. +Your *input* file should start like this: + +.. code-block:: lammps + + units real + atom_style full + boundary p p p + bond_style harmonic + angle_style harmonic + dihedral_style charmm + improper_style harmonic + + pair_style lj/cut/tip4p/long 1 2 1 1 0.105 14.0 + kspace_style pppm/tip4p 1.0e-5 + + special_bonds lj 0.0 0.0 0.5 coul 0.0 0.0 0.833 + +An important note: here, water occupies the atom types 1 and 2, +instead of 3 and 4 in the case of SiO2 from the main section +of the tutorial. + +.. |Zif-8-structure| raw:: html + + structure + +.. |Zif-8-parameters| raw:: html + + parameters + +.. |water-template| raw:: html + + water template diff --git a/docs/sphinx/source/tutorial6/figures/GCMC-dimension-dm.png b/docs/sphinx/source/tutorial6/figures/GCMC-dimension-dm.png new file mode 100644 index 000000000..dde006044 Binary files /dev/null and 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"import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "340c0f82", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"gcmc.log\")\n", + "timestep = 1\n", + "\n", + "time, vol, dens, temp = [], [], [], []\n", + "time = log.get(\"Step\")*timestep/1000 # ps\n", + "n0 = log.get(\"v_nO\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "13fc2139", 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"GCMC-number\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + " \n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,10), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = n0, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$N_\\mathrm{H20}$',\n", + " xlabel = r'$t ~ \\mathrm{(ps)}$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 25.1, 5), y_ticks=np.arange(0, 71, 10),\n", + " x_boundaries=(-1, 26), y_boundaries=(0, 70))\n", + " # Print figure\n", + " # myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorial6/figures/generate.ipynb b/docs/sphinx/source/tutorial6/figures/generate.ipynb new file mode 100644 index 000000000..21879dab0 --- /dev/null +++ b/docs/sphinx/source/tutorial6/figures/generate.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "340c0f82", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"generate.log\")\n", + "timestep = 1\n", + "\n", + "time, vol, dens, temp = [], [], [], []\n", + "for i in [0, 1, 2]:\n", + " time.append(log.get(\"Step\", run_num=i)*timestep/1000) # ps\n", + " vol.append(log.get(\"Volume\", run_num=i))\n", + " dens.append(log.get(\"Density\", run_num=i))\n", + " temp.append(log.get(\"Temp\", run_num=i))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5f64de56", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"GCMC-dimension\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,10), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=2)\n", + " \n", + " # Panel a\n", + " myplt.add_panel()\n", + " for i in [0, 1, 2]:\n", + " myplt.add_plot(x = time[i], y = temp[i], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " x = np.linspace(0, 7000)\n", + " myplt.add_plot(x = x*0+10, y = x, type = \"plot\", linewidth_data = 3,\n", + " marker = \"--\", data_color = np.array([0.3, 0.3, 0.3]), markersize = 12)\n", + " myplt.add_plot(x = x*0+40, y = x, type = \"plot\", linewidth_data = 3,\n", + " marker = \"--\", data_color = np.array([0.3, 0.3, 0.3]), markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$T ~ (\\mathrm{K})$',\n", + " xlabel = None, xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 55, 10), y_ticks=np.arange(0, 7201, 2000),\n", + " x_boundaries=(-3, 53), y_boundaries=(-500, 7000))\n", + "\n", + " # Panel b\n", + " myplt.add_panel()\n", + " for i in [0, 1, 2]:\n", + " myplt.add_plot(x = time[i], y = dens[i], type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)\n", + " x = np.linspace(0, 7000)\n", + " myplt.add_plot(x = x*0+10, y = x, type = \"plot\", linewidth_data = 3,\n", + " marker = \"--\", data_color = np.array([0.3, 0.3, 0.3]), markersize = 12)\n", + " myplt.add_plot(x = x*0+40, y = x, type = \"plot\", linewidth_data = 3,\n", + " marker = \"--\", data_color = np.array([0.3, 0.3, 0.3]), markersize = 12)\n", + " myplt.complete_panel(ylabel = r'$\\rho ~ (\\mathrm{g}/\\mathrm{cm}^3)$',\n", + " xlabel = r'$t~(\\mathrm{ps})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 55, 10), y_ticks=np.arange(2, 2.51, 0.1),\n", + " x_boundaries=(-3, 53), y_boundaries=(2, 2.5))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorials/figures/level3/water-adsorption-in-silica/generated-silica-dark.png 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b/docs/sphinx/source/tutorial6/introduction.rst new file mode 100644 index 000000000..c872ca885 --- /dev/null +++ b/docs/sphinx/source/tutorial6/introduction.rst @@ -0,0 +1,20 @@ +.. figure:: avatars/avatar-dark.webp + :height: 250 + :alt: Water molecules adsorbed in silica SiO2 porous inorganic material + :class: only-dark + :align: right + +.. figure:: avatars/avatar-light.webp + :height: 250 + :alt: Water molecules adsorbed in silica SiO2 porous inorganic material + :class: only-light + :align: right + +The objective of this tutorial is to combine molecular dynamics and +grand canonical Monte Carlo simulations to compute the adsorption of water +molecules in cracked silica material. This tutorial +illustrates the use of the grand canonical ensemble in molecular simulation, an +open ensemble where the number of atoms or molecules in the simulation box can vary. +By employing the grand canonical ensemble, we will set the chemical +potential of water within a nanoporous :math:`\text{SiO}_2` structure. + diff --git a/docs/sphinx/source/tutorial6/tutorial.rst b/docs/sphinx/source/tutorial6/tutorial.rst new file mode 100644 index 000000000..3274cc24b --- /dev/null +++ b/docs/sphinx/source/tutorial6/tutorial.rst @@ -0,0 +1,470 @@ +Generation of the silica block +============================== + +To begin this tutorial, if you are using LAMMPS--GUI, select ``Start Tutorial 6`` +from the ``Tutorials`` menu and follow the instructions. Alternatively, if you are +not using LAMMPS--GUI, create a new folder and add a file named +**generate.lmp**. Open the file in a text editor and paste in the following +content: + +.. code-block:: lammps + + units metal + boundary p p p + atom_style full + pair_style vashishta + neighbor 1.0 bin + neigh_modify delay 1 + +The main difference from some of the previous tutorials is the use of the ``Vashishta`` +pair style. The Vashishta potential implicitly models atomic bonds through +energy terms dependent on interatomic distances and angles :cite:`vashishta1990interaction`. + +Let us create a box for two atom types, ``Si`` +of mass 28.0855 g/mol and ``O`` of mass 15.9994 g/mol. +Add the following lines to **generate.lmp**: + +.. code-block:: lammps + + region box block -18.0 18.0 -9.0 9.0 -9.0 9.0 + create_box 2 box + labelmap atom 1 Si 2 O + mass Si 28.0855 + mass O 15.9994 + create_atoms Si random 240 5802 box overlap 2.0 maxtry 500 + create_atoms O random 480 1072 box overlap 2.0 maxtry 500 + +The ``create_atoms`` commands are used to place +240 Si atoms, and 480 atoms, respectively. This corresponds to +an initial density of approximately :math:`2 \, \text{g/cm}^3`, which is close +to the expected final density of amorphous silica at 300 K. + +Now, specify the pair coefficients by indicating that the first atom type +is ``Si`` and the second is ``O``: + +.. code-block:: lammps + + pair_coeff * * SiO.1990.vashishta Si O + +Ensure that the |SiO_1990_vashishta_6| file is located in the same directory as **generate.lmp**. + +.. |SiO_1990_vashishta_6| raw:: html + + SiO.1990.vashishta + +Next, add a ``dump image`` command to **generate.lmp** to follow the +evolution of the system with time: + +.. code-block:: lammps + + dump viz all image 250 myimage-*.ppm type type shiny 0.1 box no 0.01 view 180 90 zoom 3.4 size 1700 700 + dump_modify viz backcolor white acolor Si yellow adiam Si 2.5 acolor O red adiam O 2 + +.. figure:: figures/generated-silica-dark.png + :class: only-dark + :alt: Amorphous silica block + +.. figure:: figures/generated-silica-light.png + :class: only-light + :alt: Amorphous silica block + +.. container:: figurelegend + + Figure: Amorphous silica (:math:`\text{SiO}_2`). Silicon atoms are + represented in yellow, and oxygen atoms in red. + +Let us also print the box volume and system density, alongside the +temperature and total energy: + +.. code-block:: lammps + + thermo 250 + thermo_style custom step temp etotal vol density + +Finally, let us implement the annealing procedure which +consists of three consecutive runs. This procedure was inspired +by Ref. :cite:`della1992molecular`. First, to melt the system, +a :math:`10\,\text{ps}` phase at :math:`T = 6000\,\text{K}` is performed: + +.. code-block:: lammps + + velocity all create 6000 8289 rot yes dist gaussian + fix mynvt all nvt temp 6000 6000 0.1 + timestep 0.001 + run 10000 + +Next, a second phase, during which the system is cooled down from :math:`T = 6000\,\text{K}` +to :math:`T = 300\,\text{K}`, is implemented as follows: + +.. code-block:: lammps + + fix mynvt all nvt temp 6000 300 0.1 + run 30000 + +In the third step, the system is equilibrated at the final desired +conditions, :math:`T = 300\,\text{K}` and :math:`p = 1\,\text{atm}`, +using an anisotropic pressure coupling: + +.. code-block:: lammps + + unfix mynvt + + fix mynpt all npt temp 300 300 0.1 aniso 1 1 1 + run 10000 + + write_data generate.data + +Here, an anisotropic barostat is used. +Anisotropic barostats adjust the dimensions independently, which is +generally suitable for a solid phase. + +Run the simulation using LAMMPS. From the ``Charts`` window, the temperature +evolution can be observed, showing that it closely follows the desired annealing procedure. +The evolution of the box dimensions over time confirms that the box deformed during the +last stage of the simulation. After the simulation completes, the final LAMMPS topology +file called **generate.data** will be located next to **generate.lmp**. + +.. figure:: figures/GCMC-dimension-dm.png + :class: only-dark + :alt: Temperature and density of the silicon + +.. figure:: figures/GCMC-dimension.png + :class: only-light + :alt: Temperature and density of the silicon + +.. container:: figurelegend + + Figure: a) Temperature, :math:`T`, as a function of time, :math:`t`, during the annealing + of the silica system. b) System density, :math:`\rho`, during the annealing process. The vertical dashed lines + mark the transition between the different phases of the simulation. + +Cracking the silica +=================== + +Create a new file called **cracking.lmp**, and copy the following familiar lines: + +.. code-block:: lammps + + units metal + boundary p p p + atom_style full + pair_style vashishta + neighbor 1.0 bin + neigh_modify delay 1 + + read_data generate.data + + pair_coeff * * SiO.1990.vashishta Si O + + dump viz all image 250 myimage-*.ppm type type shiny 0.1 box no 0.01 view 180 90 zoom 3.4 size 1700 700 + dump_modify viz backcolor white acolor Si yellow adiam Si 2.5 acolor O red adiam O 2 + + thermo 250 + thermo_style custom step temp etotal vol density + +.. admonition:: If you are using LAMMPS-GUI + :class: gui + + Open the **cracking.lmp** file. + +Let us progressively increase the size of the box in the :math:`x` direction, +forcing the silica to deform and eventually crack. To achive this, +the ``fix deform`` command is used, with a rate +of :math:`0.005\,\text{ps}^{-1}`. Add the following lines to +the **cracking.lmp** file: + +.. code-block:: lammps + + timestep 0.001 + fix nvt1 all nvt temp 300 300 0.1 + fix mydef all deform 1 x erate 0.005 + run 50000 + + write_data cracking.data + +The ``fix nvt`` command is employed to control the temperature of the system. +As observed from the generated images, the atoms +progressively adjust to the changing box dimensions. At some point, +bonds begin to break, leading to the appearance of +dislocations. + +.. figure:: figures/cracked-dark.png + :class: only-dark + :alt: Amorphous cracked silica block + +.. figure:: figures/cracked-light.png + :class: only-light + :alt: Amorphous cracked silica block + +.. container:: figurelegend + + Figure: Block of silica after deformation. Silicon atoms are represented in yellow, + and oxygen atoms in red. The crack was induced by the + imposed deformation of the box along the :math:`x`-axis (i.e., the horizontal axis). + +Adding water +============ + +To add the water molecules to the silica, we will employ the Monte Carlo +method in the grand canonical ensemble (GCMC). In short, the system is +placed into contact with a virtual reservoir of a given chemical +potential :math:`\mu`, and multiple attempts to insert water molecules at +random positions are made. Each attempt is either accepted or rejected +based on energy considerations. For further details, please refer to +classical textbooks like Ref. :cite:`frenkel2023understanding`. + +Using hydrid potentials +----------------------- + +The first particularly of our system is that it combines water and +silica, which necessitates the use of two force fields: Vashishta (for +:math:`\text{SiO}_2`), and TIP4P (for water). Here, the TIP4P/2005 model is +employed for the water :cite:`abascal2005general`. + +Create a new file called **gcmc.lmp**, and copy the following lines into it: + +.. code-block:: lammps + + units metal + boundary p p p + atom_style full + neighbor 1.0 bin + neigh_modify delay 1 + pair_style hybrid/overlay vashishta lj/cut/tip4p/long OW HW OW-HW HW-OW-HW 0.1546 10 + kspace_style pppm/tip4p 1.0e-5 + bond_style harmonic + angle_style harmonic + +.. admonition:: If you are using LAMMPS-GUI + :class: gui + + Open the **gcmc.lmp** file. + +Combining the two force fields, Vashishta and TIP4P/2005, is achieved +using the ``hybrid/overlay`` pair style. The PPPM +solver :cite:`luty1996calculating` is specified with the ``kspace`` +command, and is used to compute the long-range Coulomb interactions associated +with ``tip4p/long``. Finally, the style for the bonds +and angles of the water molecules are defined; however, these specifications are +not critical since TIP4P/2005 is a rigid water model. + +The water molecule template called |H2O_mol_6| +must be downloaded and located next to **gcmc.lmp**. + +.. |H2O_mol_6| raw:: html + + H2O.mol + +Before going further, we need to make a few changes to our data file. +Currently, the **cracking.data** file includes only two atom types, but we require four. +Copy the previously generated **cracking.data**, and name the duplicate **cracking-mod.data**. +Make the following changes to the beginning of **cracking-mod.data** +to ensure it matches the following format (with 4 atom types, +1 bond type, 1 angle type, the proper type labels, and four masses): + +.. code-block:: lammps + + 720 atoms + 4 atom types + 1 bond types + 1 angle types + + 2 extra bond per atom + 1 extra angle per atom + 2 extra special per atom + + -22.470320800269317 22.470320800269317 xlo xhi + -8.579178758211475 8.579178758211475 ylo yhi + -8.491043517346204 8.491043517346204 zlo zhi + + Atom Type Labels + + 1 Si + 2 O + 3 OW + 4 HW + + Bond Type Labels + + 1 OW-HW + + Angle Type Labels + + 1 HW-OW-HW + + Masses + + 1 28.0855 + 2 15.9994 + 3 15.9994 + 4 1.008 + + Atoms # full + + (...) + +Doing so, we anticipate that there will be 4 atom types in the simulations, +with the oxygens and hydrogens of :math:`\text{H}_2\text{O}` having +types ``OW`` and ``HW``, respectively. There +will also be 1 bond type (``OW-HW``) and 1 angle type (``OW-HW-HW``). +The ``extra bond``, ``extra angle``, and +``extra special`` lines are here for memory allocation. + +We can now proceed to complete the **gcmc.lmp** file by adding the system definition: + +.. code-block:: lammps + + read_data cracking-mod.data + molecule h2omol H2O.mol + create_atoms 0 random 3 3245 NULL mol h2omol 4585 overlap 2.0 maxtry 50 + + group SiO type Si O + group H2O type OW HW + +After reading the data file and defining the ``h2omol`` molecule from the **H2O.mol** +file, the ``create_atoms`` command is used to include three water molecules +in the system. Then, add the following ``pair_coeff`` (and +``bond_coeff`` and ``angle_coeff``) commands +to **gcmc.lmp**: + +.. code-block:: lammps + + pair_coeff * * vashishta SiO.1990.vashishta Si O NULL NULL + pair_coeff * * lj/cut/tip4p/long 0 0 + pair_coeff Si OW lj/cut/tip4p/long 0.0057 4.42 + pair_coeff O OW lj/cut/tip4p/long 0.0043 3.12 + pair_coeff OW OW lj/cut/tip4p/long 0.008 3.1589 + pair_coeff HW HW lj/cut/tip4p/long 0.0 0.0 + bond_coeff OW-HW 0 0.9572 + angle_coeff HW-OW-HW 0 104.52 + +The force field Vashishta applies only to ``Si`` and ``O`` of :math:`\text{SiO}_2`, +and not to the ``OW`` and ``HW`` of :math:`\text{H}_2\text{O}`, thanks to the ``NULL`` parameters +used for atoms of types ``OW`` and ``HW``. Pair coefficients for the ``lj/cut/tip4p/long`` +potential are defined between O(:math:`\text{H}_2\text{O}`) and between H(:math:`\text{H}_2\text{O}`) +atoms, as well as between O(:math:`\text{SiO}_2`)-O(:math:`\text{H}_2\text{O}`) and +Si(:math:`\text{SiO}_2`)-O(:math:`\text{H}_2\text{O}`). Thus, the fluid-fluid and the +fluid-solid interactions will be adressed with by the ``lj/cut/tip4p/long`` potential. +The ``bond_coeff`` and ``angle_coeff`` commands set the ``OW-HW`` +bond length to 0.9572 Å, and the ``HW-OW-HW`` +angle to :math:`104.52^\circ`, respectively :cite:`abascal2005general`. + +Add the following lines to **gcmc.lmp** as well: + +.. code-block:: lammps + + variable oxygen atom type==label2type(atom,OW) + group oxygen dynamic all var oxygen + variable nO equal count(oxygen) + + fix shak H2O shake 1.0e-5 200 0 b OW-HW a HW-OW-HW mol h2omol + +The number of oxygen atoms from water molecules (i.e. the number of molecules) +is calculated by the ``nO`` variable. The SHAKE algorithm is used to +maintain the shape of the water molecules over time :cite:`ryckaert1977numerical, andersen1983rattle`. + +Finally, let us create images +of the system using ``dump image``: + +.. code-block:: lammps + + dump viz all image 250 myimage-*.ppm type type & + shiny 0.1 box no 0.01 view 180 90 zoom 3.4 size 1700 700 + dump_modify viz backcolor white & + acolor Si yellow adiam Si 2.5 & + acolor O red adiam O 2 & + acolor OW cyan adiam OW 2 & + acolor HW white adiam HW 1 + +GCMC simulation +--------------- + +To prepare for the GCMC simulation, let us add the +following lines into **gcmc.lmp**: + +.. code-block:: lammps + + compute ctH2O H2O temp + compute_modify thermo_temp dynamic yes + compute_modify ctH2O dynamic yes + fix mynvt1 H2O nvt temp 300 300 0.1 + fix_modify mynvt1 temp ctH2O + fix mynvt2 SiO nvt temp 300 300 0.1 + timestep 0.001 + +Two different thermostats are used for :math:`\text{SiO}_2` and :math:`\text{H}_2\text{O}`, +respectively. Using separate thermostats is usually better when the system contains +two separate species, such as a solid and a liquid. It is particularly important +to use two thermostats here because the number of water molecules will fluctuate +with time. The ``compute_modify`` command with the ``dynamic yes`` +option for water is used to specify that the number of molecules will not be constant. + +Finally, let us use the ``fix gcmc`` and perform the grand canonical Monte +Carlo steps. Add the following lines into **gcmc.lmp**: + +.. code-block:: lammps + + variable tfac equal 5.0/3.0 + fix fgcmc H2O gcmc 100 100 0 0 65899 300 -0.5 0.1 mol h2omol tfac_insert ${tfac} shake shak full_energy pressure 100 + +The ``tfac_insert`` option ensures the correct estimate for the temperature +of the inserted water molecules by taking into account the internal degrees of +freedom. Here, 100 insertion and deletion attemps are made every 100 steps. + +.. admonition:: Note + :class: non-title-info + + At a pressure of :math:`p = 100\,\text{bar}`, the chemical potential of water vapor at :math:`T = 300\,\text{K}` + can be calculated using as :math:`\mu = \mu_0 + RT \ln (\frac{p}{p_0}),` where :math:`\mu_0` is the standard + chemical potential (typically taken at a pressure :math:`p_0 = 1 \, \text{bar}`), :math:`R = 8.314\, \text{J/mol·K}` + is the gas constant, :math:`T = 300\,\text{K}` is the temperature. + +Finally, let us print some information and run for 25 ps: + +.. code-block:: lammps + + thermo 250 + thermo_style custom step temp etotal v_nO f_fgcmc[3] f_fgcmc[4] f_fgcmc[5] f_fgcmc[6] + + run 25000 + +Running this simulation using LAMMPS, one can see that the number of molecules is increasing +progressively. When using the pressure argument, LAMMPS ignores the value of the +chemical potential (here :math:`\mu = -0.5\,\text{eV}`, which corresponds roughly to +ambient conditions, i.e. to a relative humidity :math:`\text{RH} \approx 50\,\%` :cite:`gravelle2020multi`.) +The large pressure value of 100 bars was chosen to ensure that some successful +insertions of molecules would occur during the short duration of this simulation. + +.. figure:: figures/GCMC-number-dm.png + :class: only-dark + :alt: Number of water molecules from GCMC somulations + +.. figure:: figures/GCMC-number.png + :class: only-light + :alt: Number of water molecules from GCMC somulations + +.. container:: figurelegend + + Figure: Number of water molecules, :math:`N_\text{H2O}`, as a function of time, :math:`t`. + +After a few GCMC steps, the number of molecules starts increasing. Once the +crack is filled with water molecules, the total number of molecules reaches a plateau. The final number of +molecules depends on the imposed pressure, temperature, and the interaction +between water and silica (i.e. its hydrophilicity). Note that GCMC simulations +of such dense phases are usually slow to converge due to the very low probability +of successfully inserting a molecule. Here, the short simulation duration was +made possible by the use of a high pressure. + +.. figure:: figures/solvated-dark.png + :class: only-dark + :alt: Amorphous cracked silica block solvated with water + +.. figure:: figures/solvated-light.png + :class: only-light + :alt: Amorphous cracked silica block solvated with water + +.. container:: figurelegend + + Figure: Snapshot of the silica system after the adsorption of water molecules. + The oxygen atoms of the water molecules are represented in cyan, the silicon + atoms in yellow, and the oxygen atoms of the solid in red. \ No newline at end of file diff --git a/docs/sphinx/source/tutorial6/water-adsorption-in-silica.rst b/docs/sphinx/source/tutorial6/water-adsorption-in-silica.rst new file mode 100644 index 000000000..462128ddf --- /dev/null +++ b/docs/sphinx/source/tutorial6/water-adsorption-in-silica.rst @@ -0,0 +1,17 @@ +.. _gcmc-silica-label: + +Water adsorption in silica +************************** + +.. container:: hatnote + + Dealing with a varying number of molecules + +.. include:: introduction.rst +.. include:: ../shared/recommend-tutorial1.rst +.. include:: ../shared/cite.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst + diff --git a/docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar.jpg b/docs/sphinx/source/tutorial7/avatars/avatar.jpg similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar.jpg rename to docs/sphinx/source/tutorial7/avatars/avatar.jpg diff --git a/docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_dark.png b/docs/sphinx/source/tutorial7/avatars/avatar_dark.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_dark.png rename to docs/sphinx/source/tutorial7/avatars/avatar_dark.png diff --git a/docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_dark.webp b/docs/sphinx/source/tutorial7/avatars/avatar_dark.webp similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_dark.webp rename to docs/sphinx/source/tutorial7/avatars/avatar_dark.webp diff --git a/docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_light.png b/docs/sphinx/source/tutorial7/avatars/avatar_light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_light.png rename to docs/sphinx/source/tutorial7/avatars/avatar_light.png diff --git a/docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_light.webp b/docs/sphinx/source/tutorial7/avatars/avatar_light.webp similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/avatar_light.webp rename to docs/sphinx/source/tutorial7/avatars/avatar_light.webp diff --git a/docs/sphinx/source/tutorial7/exercises.rst b/docs/sphinx/source/tutorial7/exercises.rst new file mode 100644 index 000000000..7e92323d3 --- /dev/null +++ b/docs/sphinx/source/tutorial7/exercises.rst @@ -0,0 +1,137 @@ +Going further with exercises +============================ + +The binary fluid that won't mix +------------------------------- + +**1 - Create the system** + +Create a molecular simulation with two species of respective types 1 and 2. +Apply different potentials :math:`U1` and :math:`U2` on particles of +types 1 and 2, respectively, so that particles of type 1 are excluded +from the center of the box, while at the same time particles +of type 2 are excluded from the rest of the box. + +.. figure:: figures/exercice2-light.png + :alt: Particles of type 1 and 2 separated by two different potentials + :class: only-light + +.. figure:: figures/exercice2-dark.png + :alt: Particles of type 1 and 2 separated by two different potentials + :class: only-dark + +.. container:: figurelegend + + Figure: Particles of type 1 and 2 separated by two different potentials. + +**2 - Measure the PMFs** + +Using the same protocol as the one used in the tutorial +(i.e. umbrella sampling with the wham algorithm), +extract the PMF for each particle type. + +.. figure:: figures/exercice-binary-light.png + :alt: PMF in the presence of binary species + :class: only-light + +.. figure:: figures/exercice-binary-dark.png + :alt: PMF in the presence of binary species + :class: only-dark + +.. container:: figurelegend + + Figure: PMFs calculated for both atom types. + +Particles under convection +-------------------------- + +Use a similar simulation as the one from the tutorial, +with a repulsive potential in the center +of the box. Add force to the particles +and force them to flow in the :math:`x` direction. + +Re-measure the potential in the presence of the flow, and observe the difference +with the reference case in the absence of flow. + +.. figure:: figures/exercice-convection-light.png + :alt: PMF in the presence of forcing + :class: only-light + +.. figure:: figures/exercice-convection-dark.png + :alt: PMF in the presence of forcing + :class: only-dark + +.. container:: figurelegend + + Figure: PMF calculated in the presence of a net force that is inducing + the convection of the particles from left to right. + +Surface adsorption of a molecule +-------------------------------- + +Apply umbrella sampling to calculate the free energy profile +of ethanol in the direction normal to a crystal solid surface +(here made of sodium chloride). Find the |topology-ethanol| +and |parameter-ethanol|. + +.. |topology-ethanol| raw:: html + + topology files + +.. |parameter-ethanol| raw:: html + + parameter file + +Use the following lines for starting the *input.lammps*: + +.. code-block:: lammps + + units real # style of units (A, fs, Kcal/mol) + atom_style full # molecular + charge + bond_style harmonic + angle_style harmonic + dihedral_style harmonic + boundary p p p # periodic boundary conditions + pair_style lj/cut/coul/long 10 # cut-off 1 nm + kspace_style pppm 1.0e-4 + pair_modify mix arithmetic tail yes + +The PMF normal to a solid wall serves to indicate the free energy of adsorption, +which can be calculated from the difference between the PMF far +from the surface, and the PMF at the wall. + +.. figure:: figures/ethanol-light.png + :alt: Ethanol molecule next to NaCl + :class: only-light + +.. figure:: figures/ethanol-dark.png + :alt: Ethanol molecule next to NaCl + :class: only-dark + +.. container:: figurelegend + + Figure: A single ethanol molecule next to a crystal NaCl(100) wall. + +The PMF shows a minimum near the solid surface, which indicates a good +affinity between the wall and the molecule. + +.. figure:: figures/exercice-ethanol-light.png + :alt: PMF for ethanol molecule next to NaCl + :class: only-light + +.. figure:: figures/exercice-ethanol-dark.png + :alt: PMF for ethanol molecule next to NaCl + :class: only-dark + +.. container:: figurelegend + + Figure: PMF for a single ethanol molecule next to a NaCl + solid surface. The position of the wall is in :math:`x=0`. + The arrow highlights the difference between the energy of the + molecule when adsorbed to the solid surface, and + the energy far from the surface. This difference corresponds to the + free energy of adsorption. + +Alternatively to using ethanol, feel free to download the molecule of your choice, for +instance from the Automated Topology Builder (ATB). 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a/docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/exercice2-light.png b/docs/sphinx/source/tutorial7/figures/exercice2-light.png similarity index 100% rename from docs/sphinx/source/tutorials/figures/level3/free-energy-calculation/exercice2-light.png rename to docs/sphinx/source/tutorial7/figures/exercice2-light.png diff --git a/docs/sphinx/source/tutorial7/figures/free-sampling.ipynb b/docs/sphinx/source/tutorial7/figures/free-sampling.ipynb new file mode 100644 index 000000000..1ca711f9c --- /dev/null +++ b/docs/sphinx/source/tutorial7/figures/free-sampling.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "2f5b9386", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "340c0f82", + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"free-sampling.log\")\n", + "timestep = 2\n", + "\n", + "time, v_n_center, TotEng, temp = [], [], [], []\n", + "for i in [0, 1]:\n", + " time.append(log.get(\"Step\", run_num=i)*timestep/1000) # ps\n", + " temp.append(log.get(\"Temp\", run_num=i))\n", + " TotEng.append(log.get(\"TotEng\", run_num=i))\n", + " v_n_center.append(log.get(\"v_n_center\", run_num=i))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a8b977c3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"US-density-evolution\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,10), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=1)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " for i in [0]:\n", + " myplt.add_plot(x = time[i], y = v_n_center[i], type = \"plot\", linewidth_data = 3,\n", + " marker = \"o\", data_color = color3, markersize = 12)\n", + " \n", + " x = np.linspace(0, 100, 200)\n", + " myplt.add_plot(x = x, y = 35*np.exp(-x/12)+3, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = np.array([0.15, 0.15, 0.15]), markersize = 12)\n", + "\n", + " myplt.complete_panel(ylabel = r'$n_\\mathrm{center}$',\n", + " xlabel = r'$t~(\\mathrm{ps})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(0, 101, 20), y_ticks=np.arange(0, 41, 10),\n", + " x_boundaries=(-5, 105), y_boundaries=(0, 40))\n", + "\n", + " # Print figure\n", + " # myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "72dc5ab9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bulk rho = 0.00088\n" + ] + } + ], + "source": [ + "profile = np.loadtxt(path_data + \"free-sampling.dat\", skiprows=4)\n", + "\n", + "R = 8.31446 # J⋅K−1⋅mol−1\n", + "T = 119.8 # K\n", + "x_md = profile.T[1]\n", + "rho_md = profile.T[3]\n", + "rho_bulk = np.mean(rho_md[(x_md<-15) | (x_md>15)])\n", + "print(\"bulk rho =\", np.round(rho_bulk, 5))\n", + "Umd = - R*T * np.log(rho_md / rho_bulk)\n", + "Umd /= 4184 # kcal / mol" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e37e4bf7", + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 3.405 # Angstrom\n", + "epsilon = 0.238 # Kcal/mol\n", + "U0 = 1.5*epsilon # Kcal/mol\n", + "\n", + "delta = 1.0 # Angstrom\n", + "x0 = 10.0 # Angstrom\n", + "\n", + "x = np.linspace(-50, 50, 10000) # Angstrom\n", + "U = U0*np.arctan((x+x0)/delta)-U0*np.arctan((x-x0)/delta)\n", + "F = U0/(((x-x0)**2)/delta**2+1)/delta-U0/(((x+x0)**2)/delta**2+1)/delta" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d1f81b28", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"US-potential\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,10), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = x, y = U, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color0, markersize = 12)\n", + " myplt.complete_panel(xlabel = None,\n", + " ylabel = r'$U~(\\mathrm{kcal/mol})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-50, 51, 20), y_ticks=np.arange(-0., 1.3, 0.3),\n", + " x_boundaries=(-55, 55), y_boundaries=(-0.1, 1.3))\n", + "\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = x, y = F, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color0, markersize = 12)\n", + " myplt.complete_panel(xlabel = r'$x~(\\mathrm{\\AA{}})$',\n", + " ylabel = r'$F~(\\mathrm{kcal/mol/\\AA{}})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-50, 51, 20), y_ticks=np.arange(-0.4, 0.41, 0.2),\n", + " x_boundaries=(-55, 55), y_boundaries=(-0.45, 0.45))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f2968206", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"US-density\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + " \n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=2)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = x_md, y = rho_md/rho_bulk, type = \"semilogy\", linewidth_data = 3,\n", + " marker = \"o\", data_color = color3, markersize = 12)\n", + " myplt.complete_panel(xlabel = None,\n", + " ylabel = r'$\\rho / \\rho_\\mathrm{bulk}$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-50, 51, 20), #y_ticks=np.arange(0.01, 2, 0.3),\n", + " x_boundaries=(-55, 55), y_boundaries=(0.003, 5))\n", + "\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = x_md, y = Umd, type = \"plot\", linewidth_data = 3,\n", + " marker = \"o\", data_color = color3, markersize = 12)\n", + " myplt.add_plot(x = x, y = U, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color0, markersize = 12)\n", + " myplt.complete_panel(xlabel = '$x~(\\mathrm{\\AA{}})$',\n", + " ylabel = r'$U~(\\mathrm{kcal/mol})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-50, 51, 20), y_ticks=np.arange(-0., 1.3, 0.3),\n", + " x_boundaries=(-55, 55), y_boundaries=(-0.1, 1.3))\n", + "\n", + " # Print figure\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + 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a/docs/sphinx/source/tutorial7/figures/umbrella-sampling.ipynb b/docs/sphinx/source/tutorial7/figures/umbrella-sampling.ipynb new file mode 100644 index 000000000..ce970e8af --- /dev/null +++ b/docs/sphinx/source/tutorial7/figures/umbrella-sampling.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c8c9669", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2eee228a", + "metadata": {}, + "outputs": [], + "source": [ + "def read_output_wham(filename = \"umbrella-sampling.dat\"):\n", + " coor_data = []\n", + " with open(filename, 'r') as file:\n", + " section = None\n", + " for line in file:\n", + " if \"#Coor\" in line:\n", + " section = \"coor\"\n", + " elif \"#Window\" in line:\n", + " section = \"window\"\n", + " if (section == \"coor\") & (\"#\" not in line):\n", + " parts = line.split(\"\\t\")\n", + " coor_data.append([np.float32(parts[0]), np.float32(parts[1])])\n", + " return np.array(coor_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4d449723", + "metadata": {}, + "outputs": [], + "source": [ + "def create_metadata(k, filename = \"umbrella-sampling.meta\"):\n", + " f = open(filename, \"w\")\n", + " for a in np.arange(1,16):\n", + " f.write(\"umbrella-sampling.\"+str(a)+\".dat \" + str(4*a-32)+\" \"+str(k)+\"\\n\")\n", + " f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a28c02aa", + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9359f820", + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "8c49d7cd", + "metadata": {}, + "source": [ + "## Control overlap" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "942f7817", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"umbrella-sampling\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,10), dark_mode = True,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=2)\n", + " \n", + " # Panel a\n", + " myplt.add_panel()\n", + " for n in np.arange(1,26):\n", + " file = path_data + \"umbrella-sampling.\"+str(n)+\".dat\"\n", + " if os.path.exists(file):\n", + " data = np.loadtxt(file)\n", + " x0 = data[:,2][0]\n", + " x = data[:,1]\n", + " a, b = np.histogram(x, bins=50)\n", + " centers = (b[1:]+b[:-1])/2\n", + " a = a / np.sum(a)\n", + " if (n % 2 == 0):\n", + " myplt.add_plot(x = centers, y = a, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12)\n", + " else:\n", + " myplt.add_plot(x = centers, y = a, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12)" + ] + }, + { + "cell_type": "markdown", + "id": "7c9463ce", + "metadata": {}, + "source": [ + "## Compare output with imposed potential" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2bfcead0", + "metadata": {}, + "outputs": [], + "source": [ + "sigma = 3.405 # Angstrom\n", + "epsilon = 0.238 # Kcal/mol\n", + "U0 = 10*epsilon # Kcal/mol\n", + "\n", + "delta = 1.0 # Angstrom\n", + "x0 = 10.0 # Angstrom\n", + "\n", + "x = np.linspace(-50, 50, 10000) # Angstrom\n", + "U = U0*np.arctan((x+x0)/delta)-U0*np.arctan((x-x0)/delta)\n", + "F = U0/(((x-x0)**2)/delta**2+1)/delta-U0/(((x+x0)**2)/delta**2+1)/delta" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d4d18581", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"US-free-energy\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " # Initialise figure\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,5.5), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=1, n_line=1)\n", + " # Panel a\n", + " myplt.add_panel()\n", + " output_wham = read_output_wham(filename = path_data + \"umbrella-sampling.dat\")\n", + " myplt.add_plot(x = output_wham[:,0], y = output_wham[:,1], type = \"plot\", linewidth_data = 3,\n", + " marker = \"o\", data_color = color3, markersize = 12)\n", + " myplt.add_plot(x = x, y = U, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = np.array([0.2, 0.2, 0.2]), markersize = 12)\n", + "\n", + " myplt.complete_panel(xlabel = r'$x ~ (\\mathrm{\\AA})$',\n", + " ylabel = r'$U ~ (\\mathrm{kcal/mol})$', xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.set_boundaries(x_ticks=np.arange(-50, 51, 20), #y_ticks=np.arange(-0., 1.3, 0.3),\n", + " x_boundaries=(-55, 55), y_boundaries=(-1, 10))\n", + "\n", + " # Print figure\n", + " # myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/sphinx/source/tutorial7/free-energy-calculation.rst b/docs/sphinx/source/tutorial7/free-energy-calculation.rst new file mode 100644 index 000000000..6ca822e20 --- /dev/null +++ b/docs/sphinx/source/tutorial7/free-energy-calculation.rst @@ -0,0 +1,16 @@ +.. _umbrella-sampling-label: + +Free energy calculation +*********************** + +.. container:: hatnote + + Sampling a free energy barrier + +.. include:: introduction.rst +.. include:: ../shared/recommend-tutorial1.rst +.. include:: ../shared/cite.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst diff --git a/docs/sphinx/source/tutorial7/introduction.rst b/docs/sphinx/source/tutorial7/introduction.rst new file mode 100644 index 000000000..483b8045c --- /dev/null +++ b/docs/sphinx/source/tutorial7/introduction.rst @@ -0,0 +1,25 @@ +.. figure:: avatars/avatar_light.webp + :height: 250 + :alt: Lennard Jones atoms simulated with LAMMPS + :class: only-light + :align: right + +.. figure:: avatars/avatar_dark.webp + :height: 250 + :alt: Lennard Jones atoms simulated with LAMMPS + :class: only-dark + :align: right + +The objective of this tutorial is to measure the free energy profile of +particles through a barrier potential using two methods: free sampling +and umbrella sampling +:cite:`kastner2011umbrella, allen2017computer, frenkel2023understanding`. +To simplify the +process and minimize computation time, the barrier potential will be +imposed on the atoms using an additional force, mimicking the presence +of a repulsive area in the middle of the simulation box without needing +to simulate additional atoms. The procedure is valid for more complex +systems and can be adapted to many other situations, such as measuring +adsorption barriers near an interface or calculating translocation +barriers through a membrane +:cite:`wilson1997adsorption, makarov2009computer, gravelle2021adsorption, loche2022molecular, hayatifar2024probing`. \ No newline at end of file diff --git a/docs/sphinx/source/tutorial7/tutorial.rst b/docs/sphinx/source/tutorial7/tutorial.rst new file mode 100644 index 000000000..7fd9c88ff --- /dev/null +++ b/docs/sphinx/source/tutorial7/tutorial.rst @@ -0,0 +1,510 @@ +.. admonition:: What is free energy + :class: info + + The *free energy* refers to the potential energy of a system that + is available to perform work. In molecular simulations, it is + common to calculate free energy differences between different states + or conformations of a molecular system. This can be useful in understanding + the thermodynamics of a system, predicting reaction pathways, and + determining the stability of different molecular configurations. + +Method 1: Free sampling +======================= + +The most direct way to calculate a free energy profile is to extract the +partition function from a classical (i.e. unbiased) molecular dynamics +simulation, and then estimate the Gibbs free energy by using + +.. math:: + :label: eq_G + + \Delta G = -RT \ln(p/p_0), + +where :math:`\Delta G` is the free energy difference, :math:`R` is the gas constant, :math:`T` +is the temperature, :math:`p` is the pressure, and :math:`p_0` is a reference pressure. +As an illustration, let us apply this method to a simple configuration +that consists of a particles in a box in the presence of a +position-dependent repulsive force that makes the center of the box a less +favorable area to explore. + +Basic LAMMPS parameters +----------------------- + +To begin this tutorial, if you are using LAMMPS--GUI, select ``Start Tutorial 7`` +from the ``Tutorials`` menu and follow the instructions. Alternatively, if you are +not using LAMMPS--GUI, create a new folder and add a file named +**free-energy.lmp**. Open the file in a text editor and paste in the following +content: + +.. code-block:: lammps + + variable sigma equal 3.405 + variable epsilon equal 0.238 + variable U0 equal 1.5*${epsilon} + variable dlt equal 1.0 + variable x0 equal 10.0 + + units real + atom_style atomic + pair_style lj/cut 3.822 + pair_modify shift yes + boundary p p p + +Here, we begin by defining variables for the Lennard-Jones interaction +:math:`\sigma` and :math:`\epsilon` and for the repulsive potential +:math:`U`, which are :math:`U_0`, :math:`\delta`, and +:math:`x_0` [see Eqs. :eq:`eq_U`-:eq:`eq_F` below]. The cut-off value of +3.822 Å was chosen to create a Weeks-Chandler-Andersen (WCA) potential, +which is a truncated and purely repulsive LJ +potential :cite:`weeks1971role`. It was calculated as :math:`2^{1/6} \sigma`. +The potential is also shifted to be equal to 0 at the cut-off +using the ``pair_modify`` command. + +System creation and settings +---------------------------- + +Let us define the simulation box and randomly add atoms by addying the +following lines to **free-energy.lmp**: + +.. code-block:: lammps + + region myreg block -50 50 -15 15 -50 50 + create_box 1 myreg + create_atoms 1 random 200 34134 myreg overlap 3 maxtry 50 + + mass * 39.95 + pair_coeff * * ${epsilon} ${sigma} + +The variables :math:`U_0`, :math:`\delta`, and :math:`x_0`, defined in the previous subsection, are +used here to create the repulsive potential, restricting the atoms from exploring +the center of the box: + +.. math:: + :label: eq_U + + U = U_0 \left[ \arctan \left( \dfrac{x+x_0}{\delta} \right) + - \arctan \left(\dfrac{x-x_0}{\delta} \right) \right]. + +Taking the derivative of the potential with respect to :math:`x`, we obtain the expression +for the force that will be imposed on the atoms: + +.. math:: + :label: eq_F + + F = \dfrac{U_0}{\delta} \left[ \dfrac{1}{(x-x_0)^2/\delta^2+1} + - \dfrac{1}{(x+x_0)^2/\delta^2+1} \right]. + +The figure below shows the potential :math:`U` and force :math:`F` along the :math:`x`-axis. +With :math:`U_0 = 1.5 \epsilon = 0.36\,\text{kcal/mol},` :math:`U_0` is of the same order of magnitude as the +thermal energy :math:`k_\text{B} T = 0.24\,\text{kcal/mol}`, where :math:`k_\text{B} = 0.002\,\text{kcal/mol/K}` +is the Boltzmann constant and :math:`T = 119.8\,\text{K}` is the temperature +used in this simulation. Under these conditions, particles are expected to +frequently overcome the energy barrier due to thermal agitation. + +.. figure:: figures/US-potential-dm.png + :class: only-dark + :alt: Potential imporsed to the atoms + +.. figure:: figures/US-potential.png + :class: only-light + :alt: Potential imporsed to the atoms + +.. container:: figurelegend + + Figure: Potential :math:`U` given in Eq. :eq:`eq_U` (a) and force :math:`F` given in + Eq. :eq:`eq_F` (b) as functions of the coordinate :math:`x`. Here, + :math:`U_0 = 0.36~\text{kcal/mol}`, :math:`\delta = 1.0~\text{Å}`, and :math:`x_0 = 10~\text{Å}`. + +We impose the force :math:`F(x)` to the atoms in the simulation +using the ``fix addforce`` command. Add the following +lines to **free-energy.lmp**: + +.. code-block:: lammps + + variable U atom ${U0}*atan((x+${x0})/${dlt})-${U0}*atan((x-${x0})/${dlt}) + variable F atom ${U0}/((x-${x0})^2/${dlt}^2+1)/${dlt}-${U0}/((x+${x0})^2/${dlt}^2+1)/${dlt} + fix myadf all addforce v_F 0.0 0.0 energy v_U + +Next, we combine the ``fix nve`` with a ``fix langevin`` thermostat: + +.. code-block:: lammps + + fix mynve all nve + fix mylgv all langevin 119.8 119.8 500 30917 + +When combining these two commands, the MD simulation operates +in the NVT ensemble, maintaining a constant number of +atoms :math:`N`, constant volume :math:`V`, and a temperature :math:`T` that +fluctuates around a target value. + +To ensure that the equilibration time is sufficient, we will track the evolution of +the number of atoms in the central - energetically unfavorable - region, +referred to as ``mymes``, using the ``n_center`` variable: + +.. code-block:: lammps + + region mymes block -${x0} ${x0} INF INF INF INF + variable n_center equal count(all,mymes) + thermo_style custom step temp etotal v_n_center + thermo 10000 + +For visualization, use one of the following options: the ``dump image`` command to +create .ppm images of the system, or the ``dump atom`` command to write a +VMD-compatible trajectory to a file: + +.. code-block:: lammps + + # Option 1 + dump viz1 all image 50000 myimage-*.ppm type type shiny 0.1 box yes 0.01 view 180 90 zoom 6 size 1600 500 fsaa yes + dump_modify viz1 backcolor white acolor 1 cyan adiam 1 3 boxcolor black + + # Option 2 + dump viz2 all atom 50000 free-energy.lammpstrj + +Finally, let us perform an equilibration of 50000 steps, +using a timestep of :math:`2\,\text{fs}`, corresponding to a total duration of :math:`100\,\text{ps}`: + +.. code-block:: lammps + + timestep 2.0 + run 50000 + +Run the simulation with LAMMPS. The number of atoms in the +central region, :math:`n_\mathrm{center}`, reaches its equilibrium value after approximately :math:`40\,\text{ps}`. + +.. figure:: figures/US-density-evolution-dm.png + :class: only-dark + :alt: Evolution of the number of atoms + +.. figure:: figures/US-density-evolution.png + :class: only-light + :alt: Evolution of the number of atoms + +.. container:: figurelegend + + Figure: Evolution of the number of atoms :math:`n_\text{center}` in the central + region ``mymes`` as a function of time :math:`t` during equilibration. The dark line + is :math:`n_\text{center} = 22 \exp(-t/160)+5` and serves as a guide for the eyes. + Here, :math:`U_0 = 0.36~\text{kcal/mol}`, :math:`\delta = 1.0~\text{Å}`, and :math:`x_0 = 10~\text{Å}`. + +Run and data acquisition +------------------------ + +Once the system is equilibrated, we will record the density profile of +the atoms along the :math:`x`-axis using the ``ave/chunk`` command. +Add the following line to **free-energy.lmp**: + +.. code-block:: lammps + + reset_timestep 0 + + thermo 200000 + + compute cc1 all chunk/atom bin/1d x 0.0 2.0 + fix myac all ave/chunk 100 20000 2000000 cc1 density/number file free-sampling.dat + + run 2000000 + +The step count is reset to 0 using ``reset_timestep`` to synchronize it +with the output times of ``fix density/number``. Run the simulation using +LAMMPS. + +.. figure:: figures/system-dark.png + :class: only-dark + :alt: Density from umbrella sampling simulations + +.. figure:: figures/system-light.png + :class: only-light + :alt: Density from umbrella sampling simulations + +.. container:: figurelegend + + Figure: Snapshot of the system simulated during the free sampling step of the tutorial. + The atoms density is the lowest in the central part of the box, ``mymes``. Here, + :math:`U_0 = 0.36~\text{kcal/mol}`, :math:`\delta = 1.0~\text{Å}`, and :math:`x_0 = 10~\text{Å}`. + +Data analysis +------------- + +Once the simulation is complete, the density profile from **free-sampling.dat** +shows that the density in the center of the box is +about two orders of magnitude lower than inside the reservoir. +Next, we plot :math:`-R T \ln(\rho/\rho_\mathrm{bulk})` (i.e. Eq. :eq:`eq_G` where +the pressure ratio :math:`p/p_\mathrm{bulk}` is replaced by the density ratio +:math:`\rho/\rho_\mathrm{bulk}`, assuming the system behaves as an ideal gas) and compare it +with the imposed potential :math:`U` from Eq. :eq:`eq_U`. +The reference density, :math:`\rho_\text{bulk} = 0.0009~\text{Å}^{-3}`, +was estimated by measuring the density of the reservoir from the raw density +profiles. The agreement between the MD results and the imposed energy profile +is excellent, despite some noise in the central part, where fewer data points +are available due to the repulsive potential. + +.. figure:: figures/US-density-dm.png + :class: only-dark + :alt: Density from umbrella sampling simulations + +.. figure:: figures/US-density.png + :class: only-light + :alt: Density from umbrella sampling simulations + +.. container:: figurelegend + + Figure: a) Fluid density, :math:`\rho`, along the :math:`x` direction. b) Potential, :math:`U`, as a + function of :math:`x` measured using free sampling (disks) + compared to the imposed potential given in Eq. :eq:`eq_U` (line). + Here, :math:`U_0 = 0.36~\text{kcal/mol}`, :math:`\delta = 1.0~\text{Å}`, :math:`x_0 = 10~\text{Å}`, + and the measured reference density in the reservoir is :math:`\rho_\text{bulk} = 0.0009~\text{Å}^{-3}`. + +The limits of free sampling +--------------------------- + +Increasing the value of :math:`U_0` reduces the average number of atoms in the central +region, making it difficult to achieve a high-resolution free energy profile. +For example, running the same simulation with :math:`U_0 = 10 \epsilon`, +corresponding to :math:`U_0 \approx 10 k_\text{B} T`, results in no atoms exploring +the central part of the simulation box during the simulation. +In such a case, employing an enhanced sampling method is recommended, as done in the next section. + +Method 2: Umbrella sampling +=========================== + +Umbrella sampling is a biased molecular dynamics method in which +additional forces are added to a chosen atom to force it to explore the +more unfavorable areas of the system +:cite:`kastner2011umbrella, allen2017computer, frenkel2023understanding`. +Here, to encourage one +of the atoms to explore the central region of the box, we apply a +potential :math:`V` and force it to move along the :math:`x`-axis. The chosen path +is called the axis of reaction. Several simulations (called windows) +will be conducted with varying positions for the center of the applied +biasing. The results will be analyzed using the weighted histogram +analysis method (WHAM) :cite:`kumar1992weighted,kumar1995multidim`, which +allows for the removal of the biasing effect and ultimately deduces the +unbiased free energy profile. + +LAMMPS input script +------------------- + +If you are using LAMMPS--GUI, open the file named **free-energy.lmp**. +Alternatively, if you are not using LAMMPS--GUI, create a new input file +and paste in the following content: + +.. code-block:: lammps + + variable sigma equal 3.405 + variable epsilon equal 0.238 + variable U0 equal 10*${epsilon} + variable dlt equal 1.0 + variable x0 equal 10 + variable k equal 0.5 + + units real + atom_style atomic + pair_style lj/cut 3.822 + pair_modify shift yes + boundary p p p + +The first difference from the previous case is the larger value +for the repulsive potential :math:`U_0`, which makes the central area +of the system very unlikely to be visited by free particles. The second +difference is the introduction of the variable :math:`k`, which will be used for +the biasing potential. + +Let us create a simulation box with two atom types, including a single particle of type 2, +by adding the following lines to **umbrella-sampling.lmp**: + +.. code-block:: lammps + + region myreg block -50 50 -15 15 -50 50 + create_box 2 myreg + create_atoms 2 single 0 0 0 + create_atoms 1 random 199 34134 myreg overlap 3 maxtry 50 + +Next, we assign the same mass and LJ parameters to both atom types +1 and 2, and place the atoms of type 2 into a group named ``topull``: + +.. code-block:: lammps + + mass * 39.948 + pair_coeff * * ${epsilon} ${sigma} + group topull type 2 + +Then, the same potential :math:`U` and force :math:`F` are applied to all the atoms, +together with the same ``fix nve`` and ``fix langevin`` commands: + +.. code-block:: lammps + + variable U atom ${U0}*atan((x+${x0})/${dlt})-${U0}*atan((x-${x0})/${dlt}) + variable F atom ${U0}/((x-${x0})^2/${dlt}^2+1)/${dlt}-${U0}/((x+${x0})^2/${dlt}^2+1)/${dlt} + fix myadf all addforce v_F 0.0 0.0 energy v_U + + fix mynve all nve + fix mylgv all langevin 119.8 119.8 500 30917 + +Next, we perform a brief equilibration to prepare for the +umbrella sampling run: + +.. code-block:: lammps + + thermo 5000 + + # Option 1 + dump viz1 all image 50000 myimage-*.ppm type type shiny 0.1 box yes 0.01 view 180 90 zoom 6 size 1600 500 fsaa yes + dump_modify viz1 backcolor white acolor 1 cyan acolor 2 red adiam 1 3 adiam 2 3 boxcolor black + + # Option 2 + dump viz2 all atom 50000 free-energy.lammpstrj + + timestep 2.0 + run 50000 + +So far, our code resembles that of Method 1, except for the additional particle +of type 2. Particles of types 1 and 2 are identical, with the same mass +and LJ parameters. However, the particle of type 2 will also +be exposed to the biasing potential :math:`V`, which forces it to explore the +central part of the box. + +.. + TOFIX: Add a figure with one single particle exploring the central part of the system. + Add FIGURE US-system-biased Snapshot of the system simulated during the umbrella sampling + step of \hyperref[umbrella-sampling-label]{Tutorial 7}, showing type-1 atoms + in cyan and the type-2 atom in red. Only the type-2 atom explores the central part of the box, + ``mymes``, due to the additional biasing potential :math:`V`. Parmaeters are + :math:`U_0 = 2.38~\text{kcal/mol}`, :math:`\delta = 1.0~\text{Å}`, and :math:`x_0 = 10~\text{Å}`. + +Now, we create a loop with 15 steps and progressively move the center of the +bias potential by increments of 0.4 nm. Add the following lines to **umbrella-sampling.lmp**: + +.. code-block:: lammps + + variable a loop 25 + label loop + + variable xdes equal 4*${a}-32 + variable xave equal xcm(topull,x) + fix mytth topull spring tether ${k} ${xdes} 0 0 0 + + run 20000 + + fix myat1 all ave/time 10 10 100 v_xave v_xdes file umbrella-sampling.${a}.dat + + run 200000 + unfix myat1 + next a + jump SELF loop + +The ``spring`` command imposes the additional harmonic potential :math:`V` with +the previously defined spring constant :math:`k`. The center of the harmonic +potential, :math:`x_\text{des}`, successively takes values +from :math:`-28\,\text{Å}` to :math:`28\,\text{Å}`. For each value of :math:`x_\text{des}`, +an equilibration step of 40 ps is performed, followed by a step +of 400 ps during which the position of the particle of +type 2 along the :math:`x`-axis, :math:`x_\text{ave}`, is saved in data files named **umbrella-sampling.i.dat**, +where :math:`i` ranges from 1 to 15. Run the **umbrella-sampling.lmp** file using LAMMPS. + +.. admonition:: Note + :class: non-title-info + + The value of :math:`k` should be chosen with care: + if :math:`k` is too small the particle won't follow the biasing potential, + and if :math:`k` is too large there will be no overlapping between + the different windows, leading to poor reconstruction of the free energy profile. + See the section :ref:`side-note-k`. + +WHAM algorithm +-------------- + +To generate the free energy profile from the particle positions saved in +the **umbrella-sampling.i.dat** files, we use the +WHAM :cite:`kumar1992weighted,kumar1995multidim` algorithm as implemented +by Alan Grossfield :cite:`grossfieldimplementation`. You can download it +from |Alan_Grossfield|'s website. Make sure you download the WHAM code version +2.1.0 or later which introduces the ``units`` command-line option +used below. The executable called ``wham`` generated by following +the instructions from the website must be placed next to +**umbrella-sampling.lmp**. To apply the WHAM algorithm to our +simulation, we need a metadata file containing: + +.. |Alan_Grossfield| raw:: html + + Alan Grossfield + +- the paths to all the data files, +- the values of :math:`x_\text{des}`, +- the values of :math:`k`. + +Download the |umbrella_sampling_meta| file and save it next to **umbrella-sampling.lmp**. +Then, run the WHAM algorithm by typing the following command in the terminal: + +.. |umbrella_sampling_meta| raw:: html + + umbrella-sampling.meta + +.. code-block:: bash + + ./wham units real -30 30 50 1e-8 119.8 0 umbrella-sampling.meta umbrella-sampling.dat + +where -30 and 30 are the boundaries, 50 is the number of bins, 1e-8 is the tolerance, +and 119.8 is the temperature in Kelvin. A file called **umbrella-sampling.dat** is created, +containing the free energy profile in kcal/mol. The resulting PMF can be compared +with the imposed potential :math:`U`, showing excellent agreement. + +.. figure:: figures/US-free-energy-dm.png + :class: only-dark + :alt: Density from umbrella sampling simulations + +.. figure:: figures/US-free-energy.png + :class: only-light + :alt: Density from umbrella sampling simulations + +.. container:: figurelegend + + Figure: The potential, :math:`U`, as a function of :math:`x`, measured using umbrella + sampling (disks), is compared to the imposed potential given in Eq. :eq:`eq_U` + (line). Parameters are :math:`U_0 = 2.38~\text{kcal/mol}`, :math:`\delta = 1.0~\text{Å}`, + and :math:`x_0 = 10~\text{Å}`. + +Remarkably, this excellent agreement is achieved despite +the very short calculation time and the high value for the energy barrier. +Achieving similar results through free sampling would require performing extremely +long and computationally expensive simulations. + +.. _side-note-k: + +Side note: On the choice of :math:`k` +------------------------------------- + +One difficult part of umbrella sampling is choosing the value of :math:`k`. +Ideally, you want the biasing potential to be strong enough to force +the chosen atom or molecule to move along the chosen axis, while also allowing +fluctuations in its position large enough to ensure some overlap in the +probability density between neighboring positions. Here, as an illustration, +three different values of :math:`k` are tested: + +- If :math:`k` is too small, the biasing potential is too weak to + force the particle to explore the region of interest, making it + impossible to reconstruct the PMF (see panel a in the figure below). + +- If :math:`k` is "appropriate", the particle explores the entire axis, + and the probability distributions are strongly impacted by the + potential one wants to probe, as shown in panel b. + +- If :math:`k` is too large, the biasing potential dominates over the + potential one wants to probe, which reduces the + sensitivity of the method (panel c). + +.. figure:: figures/overlap-light.png + :alt: Averaged density profile + :class: only-light + +.. figure:: figures/overlap-dark.png + :alt: Averaged density profile + :class: only-dark + +.. container:: figurelegend + + Figure: Probability density for each run with :math:`k = 0.15\,\text{kcal}/\text{mol}/\mathrm{Å}^2` (a) + (a value that is too small to bring the particle into the central region), + :math:`k = 1.5\,\text{kcal}/\text{mol}/\mathrm{Å}^2` (b) (a value that allows the particle to explore + the entire path), and :math:`k = 15\,\text{kcal}/\text{mol}/\mathrm{Å}^2` (c) (a value so strong that + it becomes difficult to perceive the effect of the probed potential). diff --git a/docs/sphinx/source/tutorial8/figures/REACT-mixing-dm.png b/docs/sphinx/source/tutorial8/figures/REACT-mixing-dm.png new file mode 100644 index 000000000..a4196bebc Binary files /dev/null and b/docs/sphinx/source/tutorial8/figures/REACT-mixing-dm.png differ diff --git a/docs/sphinx/source/tutorial8/figures/REACT-mixing.png b/docs/sphinx/source/tutorial8/figures/REACT-mixing.png new file mode 100644 index 000000000..dd422c168 Binary files /dev/null and b/docs/sphinx/source/tutorial8/figures/REACT-mixing.png differ diff --git a/docs/sphinx/source/tutorial8/figures/REACT-reacting-dm.png b/docs/sphinx/source/tutorial8/figures/REACT-reacting-dm.png new file mode 100644 index 000000000..11a4dda9a Binary files /dev/null and b/docs/sphinx/source/tutorial8/figures/REACT-reacting-dm.png differ diff --git a/docs/sphinx/source/tutorial8/figures/REACT-reacting.png b/docs/sphinx/source/tutorial8/figures/REACT-reacting.png new file mode 100644 index 000000000..68adcb616 Binary files /dev/null and b/docs/sphinx/source/tutorial8/figures/REACT-reacting.png differ diff --git a/docs/sphinx/source/tutorial8/figures/mixing.ipynb b/docs/sphinx/source/tutorial8/figures/mixing.ipynb new file mode 100644 index 000000000..47fb76272 --- /dev/null +++ b/docs/sphinx/source/tutorial8/figures/mixing.ipynb @@ -0,0 +1,199 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import re\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "def extract_matrices(file_path):\n", + " with open(file_path, 'r') as file:\n", + " content = file.readlines()\n", + " pattern = re.compile(r'^\\s*(\\d+)\\s+([\\d.]+)\\s+([\\d.]+)\\s+([\\d.]+)\\s+(-?[\\d.]+)\\s+([\\d.]+)\\s*$')\n", + " matrices = []\n", + " current_matrix = []\n", + " for line in content:\n", + " match = pattern.match(line)\n", + " if match:\n", + " row = [float(match.group(i)) if i != 1 else int(match.group(i)) for i in range(1, 7)]\n", + " current_matrix.append(row)\n", + " elif current_matrix:\n", + " matrices.append(current_matrix)\n", + " current_matrix = []\n", + " if current_matrix:\n", + " matrices.append(current_matrix)\n", + " return matrices" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# For some reason, lammps_logfile does not work, so a custom script is used\n", + "file_path = path_data + \"mixing.log\"\n", + "data_matrixes = extract_matrices(file_path)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"REACT-mixing\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + "\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = np.array(data_matrixes[0])[:,0]/1000,\n", + " y = np.array(data_matrixes[0])[:,5],\n", + " type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = 2, markersize = 12)\n", + " myplt.add_plot(x = np.array(data_matrixes[1])[:,0]/1000+np.max(np.array(data_matrixes[0])[:,0])/1000,\n", + " y = np.array(data_matrixes[1])[:,5],\n", + " type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = 2, markersize = 12)\n", + " x = np.linspace(-1000, 1000)\n", + " myplt.add_plot(x = x*0+5.6, y = x, type = \"plot\", linewidth_data = 1.5,\n", + " marker = \":\", data_color = color0, markersize = 12)\n", + " myplt.set_boundaries(x_boundaries=(-1, 17), x_ticks=np.arange(0, 15.1, 5),\n", + " y_boundaries=(0, 1), y_ticks=np.arange(0, 1.01, 0.25))\n", + " myplt.complete_panel(ylabel = r'$\\rho (\\mathrm{g}/\\mathrm{cm}^3)$',\n", + " xlabel = r'$t ~ \\mathrm{(ps)}$',\n", + " xpad = 10, legend=False, handlelength_legend=1)\n", + "\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = np.array(data_matrixes[0])[:,0]/1000,\n", + " y = np.array(data_matrixes[0])[:,3]/1000,\n", + " type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = 2, markersize = 12)\n", + " myplt.add_plot(x = np.array(data_matrixes[1])[:,0]/1000+np.max(np.array(data_matrixes[0])[:,0])/1000,\n", + " y = np.array(data_matrixes[1])[:,3]/1000,\n", + " type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = 2, markersize = 12)\n", + " x = np.linspace(-1000, 1000)\n", + " myplt.add_plot(x = x*0+5.6, y = x, type = \"plot\", linewidth_data = 1.5,\n", + " marker = \":\", data_color = color0, markersize = 12)\n", + " myplt.set_boundaries(x_boundaries=(-1, 17), x_ticks=np.arange(0, 15.1, 5),\n", + " y_boundaries=(17, 22.2), y_ticks=np.arange(17, 22.1, 1))\n", + " myplt.complete_panel(ylabel = r'$E (\\mathrm{MJ}/\\mathrm{mol})$',\n", + " xlabel = r'$t ~ \\mathrm{(ps)}$',\n", + " xpad = 10, legend=False, handlelength_legend=1)\n", + "\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/sphinx/source/tutorial8/figures/polymerize.ipynb b/docs/sphinx/source/tutorial8/figures/polymerize.ipynb new file mode 100644 index 000000000..d90505b60 --- /dev/null +++ b/docs/sphinx/source/tutorial8/figures/polymerize.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import sys, os, git, lammps_logfile\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "current_path = os.getcwd()\n", + "git_repo = git.Repo(current_path, search_parent_directories=True)\n", + "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", + "path_in_folder = current_path[len(git_path)+1:]\n", + "sys.path.append(git_path + \"/.dependencies/pyplot-perso\")\n", + "from plttools import PltTools\n", + "path_figures = current_path # git_path + \"/figures/\"\n", + "path_data = git_path + \"/.dependencies/lammpstutorials-inputs/\"+path_in_folder.split('/')[-2]+\"/\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "color0_light = np.array([0.5, 0.5, 0.5])\n", + "color1_light = np.array([1, 0.682, 0.286])\n", + "color2_light = np.array([0.008, 0.294, 0.478])\n", + "color3_light = np.array([0.267, 0.647, 0.761])\n", + "\n", + "color0_dark = np.array([0.5, 0.5, 0.5])\n", + "color1_dark = np.array([1, 0.8, 0.5])\n", + "color2_dark = np.array([0.24, 0.58, 1.0])\n", + "color3_dark = np.array([0.4, 0.75, 0.85])\n", + "\n", + "colors_light = {\n", + " \"color0\": color0_light,\n", + " \"color1\": color1_light,\n", + " \"color2\": color2_light,\n", + " \"color3\": color3_light,\n", + "}\n", + "\n", + "colors_dark = {\n", + " \"color0\": color0_dark,\n", + " \"color1\": color1_dark,\n", + " \"color2\": color2_dark,\n", + " \"color3\": color3_dark,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "log = lammps_logfile.File(path_data + \"polymerize.log\")\n", + "timestep = 0.001 # ps\n", + "time = log.get(\"Step\")*timestep\n", + "Temp = log.get(\"Temp\")\n", + "product1 = log.get(\"f_rxn[1]\")\n", + "product2 = log.get(\"f_rxn[2]\")\n", + "product3 = log.get(\"f_rxn[3]\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "filename = \"REACT-reacting\"\n", + "\n", + "for dark_mode in [True, False]:\n", + "\n", + " colors = colors_dark if dark_mode else colors_light\n", + " globals().update(colors)\n", + "\n", + " myplt = PltTools()\n", + " myplt.prepare_figure(fig_size = (18,7), dark_mode = dark_mode,\n", + " transparency = True, use_serif=True, n_colone=2)\n", + "\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = Temp, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = 2, markersize = 12)\n", + " myplt.set_boundaries(x_boundaries=(-1, 26), # x_ticks=np.arange(0, 1800, 300)\n", + " y_boundaries=(500, 580), y_ticks=np.arange(500, 581, 20))\n", + " myplt.complete_panel(ylabel = r'$T ~ (\\mathrm{K})$',\n", + " xlabel = r'$t ~ \\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + "\n", + " myplt.add_panel()\n", + " myplt.add_plot(x = time, y = product1, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color0, markersize = 12, data_label = \"Rxn 1\")\n", + " myplt.add_plot(x = time, y = product2, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color1, markersize = 12, data_label = \"Rxn 2\")\n", + " myplt.add_plot(x = time, y = product3, type = \"plot\", linewidth_data = 3,\n", + " marker = \"-\", data_color = color2, markersize = 12, data_label = \"Rxn 3\")\n", + " #data_label = r'$E_{12}$')\n", + " myplt.set_boundaries(x_boundaries=(-1, 26), # x_ticks=np.arange(0, 1800, 300)\n", + " y_boundaries=(-5, 85), y_ticks=np.arange(0, 81, 20))\n", + " myplt.complete_panel(ylabel = r'$\\mathrm{N}_r$', xlabel = r'$t ~ \\mathrm{(ps)}$',\n", + " xpad = 10, legend=True, handlelength_legend=1)\n", + " myplt.add_subplotlabels()\n", + " myplt.save_figure(filename = filename, saving_path = path_figures)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/sphinx/source/tutorial8/introduction.rst b/docs/sphinx/source/tutorial8/introduction.rst new file mode 100644 index 000000000..4dccec16c --- /dev/null +++ b/docs/sphinx/source/tutorial8/introduction.rst @@ -0,0 +1,19 @@ +.. figure:: avatars/avatar_light.webp + :height: 250 + :alt: Lennard Jones atoms simulated with LAMMPS + :class: only-light + :align: right + +.. figure:: avatars/avatar_dark.webp + :height: 250 + :alt: Lennard Jones atoms simulated with LAMMPS + :class: only-dark + :align: right + +The goal of this tutorial is to create a model of a carbon nanotube (CNT) embedded +in a polymer melt made of polystyrene (PS) (Fig.~\ref{fig:REACT}). The +REACTER protocol is used to simulate the polymerization of styrene monomers, and the +polymerization reaction is followed in time :cite:`gissinger2017polymer, gissinger2020reacter, gissinger2024molecular`. +In contrast with AIREBO :ref:`carbon-nanotube-label` +and ReaxFF :ref:`reactive-silicon-dioxide-label`, the REACTER +protocol relies on the use of a *classical* force field. \ No newline at end of file diff --git a/docs/sphinx/source/tutorial8/reactive-molecular-dynamics.rst b/docs/sphinx/source/tutorial8/reactive-molecular-dynamics.rst new file mode 100644 index 000000000..8f57e48cd --- /dev/null +++ b/docs/sphinx/source/tutorial8/reactive-molecular-dynamics.rst @@ -0,0 +1,16 @@ +.. _bond-react-label: + +Reactive Molecular Dynamics +*************************** + +.. container:: hatnote + + Modeling reaction using the REACTER protocol + +.. include:: introduction.rst +.. include:: ../shared/recommend-tutorial1.rst +.. include:: ../shared/cite.rst +.. include:: ../shared/versionLAMMPS.rst +.. include:: tutorial.rst +.. include:: ../shared/access-the-files.rst +.. include:: exercises.rst diff --git a/docs/sphinx/source/tutorial8/tutorial.rst b/docs/sphinx/source/tutorial8/tutorial.rst new file mode 100644 index 000000000..b00b6d385 --- /dev/null +++ b/docs/sphinx/source/tutorial8/tutorial.rst @@ -0,0 +1,329 @@ +Creating the system +=================== + +To begin this tutorial, select ``Start Tutorial 8`` from the +``Tutorials`` menu of LAMMPS--GUI and follow the instructions. +The editor should display the following content corresponding to **mixing.lmp**: + +.. code-block:: lammps + + units real + boundary p p p + atom_style full + + kspace_style pppm 1.0e-5 + pair_style lj/class2/coul/long 8.5 + angle_style class2 + bond_style class2 + dihedral_style class2 + improper_style class2 + + pair_modify tail yes mix sixthpower + special_bonds lj/coul 0 0 1 + +The ``class2`` styles compute a 6/9 Lennard-Jones potential :cite:`sun1998compass`. +The ``class2`` bond, angle, dihedral, and improper styles are used as +well, see the documentation for a description of their respective potentials. +The ``mix sixthpower`` imposes the following mixing rule for the calculation +of the cross coefficients: + +.. math:: + + \sigma_{ij} & = & 2^{-1/6} (\sigma^6_i+\sigma_j^6)^{1/6}, ~ \text{and} + + \epsilon_{ij} & = & \dfrac{2 \sqrt{\epsilon_i \epsilon_j} \sigma^3_i \sigma^3_j}{\sigma^6_i+\sigma_j^6}. + +Let us read the |CNT_data_8| file, which contains a periodic single-walled +CNT. Add the following line to **mixing.lmp**: + +.. |CNT_data_8| raw:: html + + CNT.data + +.. code-block:: lammps + + read_data CNT.data extra/special/per/atom 20 + +The CNT is approximately :math:`1.1~\text{nm}` in diameter and :math:`1.6\,\text{nm}` in length, oriented +along the :math:`x`-axis. The simulation box is initially 12.0 nm in the two other dimensions before densification, +making it straightforward to fill the box with styrene. To add 200 styrene molecules to the simulation box, +we will use the |styrene_mol_8| molecule template file. Include the following commands to **mixing.lmp**: + +.. |styrene_mol_8| raw:: html + + styrene.mol + +.. code-block:: lammps + + molecule styrene styrene.mol + create_atoms 0 random 200 8305 NULL overlap 2.75 maxtry 500 mol styrene 7687 + +Finally, let us use the ``minimize`` command to reduce the potential energy of the system: + +.. code-block:: lammps + + minimize 1.0e-4 1.0e-6 100 1000 + reset_timestep 0 + +Then, let us densify the system to a target value of :math:`0.9~\text{g/cm}^3` +by manually shrinking the simulation box at a constant rate. The dimension parallel +to the CNT axis is maintained fixed because the CNT is periodic in that direction. +Add the following commands to **mixing.lmp**: + +.. code-block:: lammps + + velocity all create 530 9845 dist gaussian rot yes + fix mynvt all nvt temp 530 530 100 + + fix mydef all deform 1 y erate -0.0001 z erate -0.0001 + variable rho equal density + fix myhal all halt 10 v_rho > 0.9 error continue + + thermo 200 + thermo_style custom step temp pe etotal press density + + run 9000 + +The ``fix halt`` command is used to stop the box shrinkage once the +target density is reached. + +For the next stage of the simulation, we will use ``dump image`` to +output images every 200 steps: + +.. code-block:: lammps + + dump viz all image 200 myimage-*.ppm type type shiny 0.1 box no 0.01 size 1000 1000 view 90 0 zoom 1.8 fsaa yes bond atom 0.5 + dump_modify viz backcolor white acolor cp gray acolor c=1 gray acolor c= gray acolor c1 deeppink & + acolor c2 deeppink acolor c3 deeppink adiam cp 0.3 adiam c=1 0.3 adiam c= 0.3 adiam c1 0.3 & + adiam c2 0.3 adiam c3 0.3 acolor hc white adiam hc 0.15 + +For the following :math:`10~\text{ps}`, let us equilibrate the densified system +in the constant-volume ensemble, and write the final state of the +system in a file named **mixing.data**: + +.. code-block:: lammps + + unfix mydef + unfix myhal + reset_timestep 0 + + group CNT molecule 1 + fix myrec CNT recenter NULL 0 0 units box shift all + + run 10000 + + write_data mixing.data + +For visualization purposes, the atoms from the CNT ``group`` is moved +to the center of the box using ``fix recenter``. +As the time progresses, the system density, +:math:`\rho`, gradually converges toward the target value +of :math:`0.8`\,g/cm:math:`^3`. +Meanwhile, the total energy of the system initially evolves rapidly, reflecting the +densification process, and then eventually stabilizes. + +.. figure:: figures/REACT-mixing-dm.png + :class: only-dark + :alt: Evolution of the density REACTER protocole + +.. figure:: figures/REACT-mixing.png + :class: only-light + :alt: Evolution of the density REACTER protocole + +.. container:: figurelegend + + Figure: a) Evolution of the density, :math:`\rho`, as a function of the + time, :math:`t`, during equilibration of the system. b) Evolution of the total + energy, :math:`E`, of the system. + The vertical dashed lines mark the transition between the different + phases of the simulation. + +Reaction templates +------------------ + +The REACTER protocol enables the modeling of chemical reactions using +classical force fields. The user must provide a molecule template for the reactants, +a molecule template for the products, and a ``reaction map`` file that +provides an atom mapping between the two templates. The reaction map file also includes +additional information, such as which atoms act as initiators for the reaction and which +serve as edge atoms to connect the rest of a long polymer chain in the simulation. + +There are three reactions to define: (1) the polymerization of two styrene monomers, +(2) the addition of a styrene monomer to the end of a growing polymer chain, and (3) the +linking of two polymer chains. Download the three files associated with each reaction. +The first reaction uses the prefix ``M-M`` for the pre-reaction template, +post-reaction template, and reaction map file: + +- |M_M_pre_mol_8|, +- |M_M_post_mol_8|, +- |M_M_rxnmap_8|. + +The second reaction uses the prefix ``M-P``, + +- |M_P_pre_mol_8|, +- |M_P_post_mol_8|, +- |M_P_rxnmap_8|. + +The third reaction uses the prefix ``P-P``, + +- |P_P_pre_mol_8|, +- |P_P_post_mol_8|, +- |P_P_rxnmap_8|. + +Here, the file names for each reaction use the abbreviation `M' for monomer and `P' +for polymer. + +.. |M_M_pre_mol_8| raw:: html + + M-M_pre.mol + +.. |M_M_post_mol_8| raw:: html + + M-M_post.mol + +.. |M_M_rxnmap_8| raw:: html + + M-M.rxnmap + +.. |M_P_pre_mol_8| raw:: html + + M-P_pre.mol + +.. |M_P_post_mol_8| raw:: html + + M-P_post.mol + +.. |M_P_rxnmap_8| raw:: html + + M-P.rxnmap + +.. |P_P_pre_mol_8| raw:: html + + P-P_pre.mol + +.. |P_P_post_mol_8| raw:: html + + P-P_post.mol + +.. |P_P_rxnmap_8| raw:: html + + P-P.rxnmap + +Simulating the reaction +----------------------- + +The first step, before simulating the reaction, is to import the previously +generated configuration. Open the file named **polymerize.lmp**, +which should contain the following lines: + +.. code-block:: lammps + + units real + boundary p p p + atom_style full + + kspace_style pppm 1.0e-5 + pair_style lj/class2/coul/long 8.5 + angle_style class2 + bond_style class2 + dihedral_style class2 + improper_style class2 + + pair_modify tail yes mix sixthpower + special_bonds lj/coul 0 0 1 + + read_data mixing.data extra/bond/per/atom 5 extra/angle/per/atom 15 extra/dihedral/per/atom 15 extra/improper/per/atom 25 extra/special/per/atom 25 + +Here, the ``read_data`` command is used to import the +previously generated **mixing.data** file. All other commands +have been introduced in earlier parts of the tutorial. + +Then, let us import all six molecules templates using the ``molecule`` command: + +.. code-block:: lammps + + molecule mol1 M-M_pre.mol + molecule mol2 M-M_post.mol + molecule mol3 M-P_pre.mol + molecule mol4 M-P_post.mol + molecule mol5 P-P_pre.mol + molecule mol6 P-P_post.mol + +In order to follow the evolution of the reaction with time, let us generate images +of the system using ``dump image``: + +.. code-block:: lammps + + dump viz all image 200 myimage-*.ppm type type shiny 0.1 box no 0.01 size 1000 1000 view 90 0 zoom 1.8 fsaa yes bond atom 0.5 + dump_modify viz backcolor white acolor cp gray acolor c=1 gray acolor c= gray acolor c1 deeppink acolor c2 gray acolor c3 deeppink & + adiam cp 0.3 adiam c=1 0.3 adiam c= 0.3 adiam c1 0.3 adiam c2 0.3 adiam c3 0.3 acolor hc white adiam hc 0.15 + +Let us use ``fix bond/react`` by adding the following +line to **polymerize.lmp**: + +.. code-block:: lammps + + fix rxn all bond/react stabilization yes statted_grp 0.03 react R1 all 1 0 3.0 mol1 mol2 M-M.rxnmap & + react R2 all 1 0 3.0 mol3 mol4 M-P.rxnmap react R3 all 1 0 5.0 mol5 mol6 P-P.rxnmap + +With the ``stabilization`` keyword, the ``bond/react`` command will +stabilize the atoms involved in the reaction using the ``nve/limit`` +command with a maximum displacement of :math:`0.03\,\text{Å}`. By default, +each reaction is stabilized for 60 time steps. Each ``react`` keyword +corresponds to a reaction, e.g., a transformation of ``mol1`` into ``mol2`` +based on the atom map **M-M.rxnmap**. Implementation details about each reaction, +such as the reaction distance cutoffs and the frequency with which to search for +reaction sties, are also specified in this command. + +ADD REACT-final FIGURE: Final configuration. +The atoms from the formed polymer named ``c1``, ``c2``, and +``c3`` are colored in pink. + +.. admonition:: Note + :class: non-title-info + + The command ``fix bond/react`` creates several groups of atoms that are dynamically updated + to track which atoms are being stabilized and which atoms are undergoing + dynamics with the system-wide time integrator (here, ``fix nvt``). + When reaction stabilization is employed, there should not be a time integrator acting on + the group ``all``. Instead, the group of atoms not currently + undergoing stabilization is named by appending ``_REACT`` to the user-provided prefix. + +Add the following commands to **polymerize.lmp** to operate in the NVT ensemble +while ensuring that the CNT remains centered in the simulation box: + +.. code-block:: lammps + + fix mynvt statted_grp_REACT nvt temp 530 530 100 + group CNT molecule 1 2 3 + fix myrec CNT recenter NULL 0 0 shift all + + thermo 1000 + thermo_style custom step temp press density f_rxn[*] + + run 25000 + +Here, the ``thermo custom`` command is used +to print the cumulative reaction counts from ``fix rxn``. +Run the simulation using LAMMPS. As the simulation progresses, polymer chains are +observed forming. During this reaction process, the +temperature of the system remains well-controlled, +while the number of reactions, :math:`N_r`, increases with time. + +.. figure:: figures/REACT-reacting-dm.png + :class: only-dark + :alt: Evolution of reacting species + +.. figure:: figures/REACT-reacting.png + :class: only-light + :alt: Evolution of reacting species + +.. container:: figurelegend + + Figure: a) Evolution of the system temperature, :math:`T`, as a function of + the time, :math:`t`, during the polymerization step. b) Evolution of + the three reaction counts, corresponding respectively to + the polymerization of two styrene monomers (Rxn 1), the addition of a styrene + monomer to the end of a growing polymer chain (Rxn 2), and to the linking + of two polymer chains (Rxn 3). + diff --git a/docs/sphinx/source/tutorials/bash/bash-tutorial.rst b/docs/sphinx/source/tutorials/bash/bash-tutorial.rst deleted file mode 100644 index 92f2c8797..000000000 --- a/docs/sphinx/source/tutorials/bash/bash-tutorial.rst +++ /dev/null @@ -1,223 +0,0 @@ -.. _bash-label: - -Bash tutorial -************* - -.. container:: hatnote - - Efficiently handle multiple simulations - -.. container:: justify - - Bash scripts can help launch multiple simulations and interact with - LAMMPS input scripts. This can be useful for instance to efficiently - explore a parameter space. - -.. include:: ../../non-tutorials/needhelp.rst - -.. include:: ../../non-tutorials/2Aug2023.rst - -.. container:: justify - - In this tutorial, a simple LAMMPS input script is launched - multiple times using a Bash script. At each iteration, - the number of particles is increased. - -.. figure:: ../figures/bash/bash-tutorial/banner-dark.png - :alt: Image of the lammps LJ fluid - :class: only-dark - -.. figure:: ../figures/bash/bash-tutorial/banner-light.png - :alt: Image of the lammps LJ fluid - :class: only-light - -.. container:: figurelegend - - Binary Lennard-Jones fluid with 1500 particles of type 1 (small spheres) - and an increasing number of particles of type 2 (large spheres), - from 1 (left) to 729 (right). - -Files preparation ------------------ - -.. container:: justify - - To follow this tutorial, |input_file| this simple LAMMPS input file - from :ref:`lennard-jones-label`. - -.. |input_file| raw:: html - - download - -.. container:: justify - - The only changes that were made in this input, compared to :ref:`lennard-jones-label`, - are the use of a variable *nb2* to control the number of particles of type 2: - -.. code-block:: lammps - - create_atoms 1 random 1500 921342 simulation_box overlap 1 maxtry 500 - create_atoms 2 random ${nb2} 225469 simulation_box overlap 1 maxtry 500 - -.. container:: justify - - Normally, we would specify the value of *nb2* within *input.lammps* using - something like *variable nb2 equal 10*, but here instead the value of - *nb2* will be specified externally using *Bash*. - -.. container:: justify - - Another change from :ref:`lennard-jones-label` is that the *overlap* - and *maxtry* keywords were added to ensure that the desired number - of atoms will always be created. - -Pass a variable to a LAMMPS input ---------------------------------- - -.. container:: justify - - The value of nb2 can be specified externally by using *-var* keyword. In the - terminal, call LAMMPS using: - -.. code-block:: bash - - lmp -in input.lammps -var nb2 81 - -.. container:: justify - - Here, *lmp* refers to the compiled LAMMPS version and a value of 81 is - given to the variable *nb2*. Looking at the log file generated during the - simulation, it should be clear that 81 particles of type 2 were created - in addition to the 1500 particles of type 1: - -.. code-block:: bash - - Created 1500 atoms - (...) - Created 81 atoms - -Make a loop ------------ - -.. container:: justify - - Let us use Bash to launch LAMMPS multiple times with different values of the - variable *nb2*. - -.. container:: justify - - Next to the downloaded *input.lammps*, create a new empty file called - *launch_LAMMPS.sh*, and copy the following lines into it. - -.. code-block:: bash - - #!/bin/bash - set -e - - for nb2 in 1 9 81 729 - do - echo 'nb2 = '${nb2} - done - -.. container:: justify - - The first line *#!/bin/bash* indicates that this is a Bash script, - and *set -e* tells Bash to exit immediately in the case of an error. - -.. container:: justify - - Then, within the for loop, the variable $nb2$ takes on the values 1, 9, 81, - and 729 successively, and the *echo* command prints its value at each step. - This Bash script can be executed by typing in a terminal: - -.. code-block:: bash - - bash launch_LAMMPS.sh - -.. container:: justify - - This should return: - -.. code-block:: bash - - nb2 = 1 - nb2 = 9 - nb2 = 81 - nb2 = 729 - -.. container:: justify - - Let us complete the script by calling LAMMPS at each step of the loop: - -.. code-block:: bash - - #!/bin/bash - set -e - - for nb2 in 1 9 81 729 - do - echo 'nb2 = '${nb2} - lmp -in input.lammps -var nb2 ${nb2} - folder=nb${nb2} - mkdir ${folder} - cp dump.lammpstrj ${folder} - done - -.. container:: justify - - As always, replace *lmp* with the proper path to your LAMMPS executable. - -.. container:: justify - - The command starting with *lmp* calls the LAMMPS input *input.lammps*, - while also passing the value of *nb2* to the LAMMPS variable named *nb2*. - -.. container:: justify - - Once the LAMMPS simulation is over, a folder named *nbi*, with i = 1, 9, 81, - or 729 is created by the *mkdir* command, and - the resulting *lammpstrj* file - is copied into it by the *cp* command. - -.. container:: justify - - An alternative way to launch *launch_LAMMPS.sh* is to make it executable - first: - -.. code-block:: bash - - chmod +x launch_LAMMPS.sh - ./launch_LAMMPS.sh - -Pass a random number --------------------- - -.. container:: justify - - Some LAMMPS commands use seeds, such as the *create_atoms* command. - To generate statistically independent simulations, it is sometimes - useful to launch the same input several times using a different seed. - -.. container:: justify - - Within *input.lammps*, add a new variable called *rdm* to the second - *create_atoms* command: - -.. code-block:: lammps - - create_atoms 2 random ${nb2} ${rdm2} simulation_box overlap 1 maxtry 500 - -.. container:: justify - - Then, within the bash script *launch_LAMMPS.sh*, modify the command line as - follows: - -.. code-block:: bash - - ${lmp} -in input.lammps -var nb2 ${nb2} -var rdm2 $RANDOM - -.. container:: justify - - The *-var rdm2 $RANDOM* was added to pass a random number to - the LAMMPS input file. This way, every time the same input file is used, - a different configuration will be created by LAMMPS. \ No newline at end of file diff --git a/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/banner-dark.png b/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/banner-dark.png deleted file mode 100644 index 51538239e..000000000 Binary files a/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/banner-dark.png and /dev/null differ diff --git a/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/banner-light.png b/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/banner-light.png deleted file mode 100644 index 22d037979..000000000 Binary files a/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/banner-light.png and /dev/null differ diff --git a/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/state.vmd b/docs/sphinx/source/tutorials/figures/bash/bash-tutorial/state.vmd deleted file mode 100644 index 1bcde130c..000000000 --- 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material change ambient perso1 0.170000 - material change diffuse perso1 0.490000 - material change specular perso1 0.140000 - material change shininess perso1 0.310000 - material change mirror perso1 0.000000 - material change opacity perso1 1.000000 - material change outline perso1 0.000000 - material change outlinewidth perso1 0.000000 - material change transmode perso1 0.000000 -} -vmdrestoremymaterials -# Atom selection macros -atomselect macro at {resname ADE A THY T -} -atomselect macro acidic {resname ASP GLU -} -atomselect macro cyclic {resname HIS PHE PRO TRP TYR -} -atomselect macro acyclic {protein and not cyclic -} -atomselect macro aliphatic {resname ALA GLY ILE LEU VAL -} -atomselect macro alpha {protein and name CA -} -atomselect macro amino {protein -} -atomselect macro aromatic {resname HIS PHE TRP TYR -} -atomselect macro basic {resname ARG HIS LYS HSP -} -atomselect macro bonded {numbonds > 0 -} -atomselect macro buried {resname ALA LEU VAL ILE PHE CYS MET TRP -} -atomselect macro cg {resname CYT C GUA G -} -atomselect macro charged {basic or acidic -} -atomselect macro hetero {not (protein or nucleic) -} -atomselect macro hydrophobic {resname ALA LEU VAL ILE PRO PHE MET TRP -} -atomselect macro small {resname ALA GLY SER -} -atomselect macro medium {resname VAL THR ASP ASN PRO CYS ASX PCA HYP -} -atomselect macro large {protein and not (small or medium) -} -atomselect macro neutral {resname VAL PHE GLN TYR HIS CYS MET TRP ASX GLX PCA HYP -} -atomselect macro polar {protein and not hydrophobic -} -atomselect macro purine {resname ADE A GUA G -} -atomselect macro pyrimidine {resname CYT C THY T URA U -} -atomselect macro surface {protein and not buried -} -atomselect macro lipid {resname DLPE DMPC DPPC GPC LPPC PALM PC PGCL POPC POPE -} -atomselect macro lipids {lipid -} -atomselect macro ion {resname AL BA CA CAL CD CES CLA CL CO CS CU CU1 CUA HG IN IOD K LIT MG MN3 MO3 MO4 MO5 MO6 NA NAW OC7 PB POT PT RB SOD TB TL WO4 YB ZN ZN1 ZN2 -} -atomselect macro ions {ion -} -atomselect macro sugar {resname AGLC -} -atomselect macro solvent {not (protein or sugar or nucleic or lipid) -} -atomselect macro glycan {resname NAG BGLN FUC AFUC MAN AMAN BMA BMAN -} -atomselect macro carbon {name "C.*" and not ion -} -atomselect macro hydrogen {name "[0-9]?H.*" -} -atomselect macro nitrogen {name "N.*" -} -atomselect macro oxygen {name "O.*" -} -atomselect macro sulfur {name "S.*" and not ion -} -atomselect macro noh {not hydrogen -} -atomselect macro heme {resname HEM HEME -} -atomselect macro conformationall {altloc "" -} -atomselect macro conformationA {altloc "" or altloc "A" -} -atomselect macro conformationB {altloc "" or altloc "B" -} -atomselect macro conformationC {altloc "" or altloc "C" -} -atomselect macro conformationD {altloc "" or altloc "D" -} -atomselect macro conformationE {altloc "" or altloc "E" -} -atomselect macro conformationF {altloc "" or altloc "F" -} -atomselect macro drude {type DRUD or type LP -} -atomselect macro unparametrized beta<1 -atomselect macro addedmolefacture {occupancy 0.8} -atomselect macro qwikmd_protein {(not name QWIKMDDELETE and protein)} -atomselect macro qwikmd_nucleic {(not name QWIKMDDELETE and nucleic)} -atomselect macro qwikmd_glycan {(not name QWIKMDDELETE and glycan)} -atomselect macro qwikmd_lipid {(not name QWIKMDDELETE and lipid)} -atomselect macro qwikmd_hetero {(not name QWIKMDDELETE and hetero and not qwikmd_protein and not qwikmd_lipid and not qwikmd_nucleic and not qwikmd_glycan and not water)} -# Display settings -display eyesep 0.065000 -display focallength 2.000000 -display height 1.000000 -display distance -2.000000 -display projection Orthographic -display nearclip set 0.500000 -display farclip set 10.000000 -display depthcue off -display cuestart 0.500000 -display cueend 10.000000 -display cuestart 0.500000 -display cueend 10.000000 -display cuedensity 0.320000 -display cuemode Exp2 -display shadows off -display ambientocclusion off -display aoambient 0.800000 -display aodirect 0.300000 -display dof off -display dof_fnumber 64.000000 -display dof_focaldist 0.700000 -mol new dump.lammpstrj type lammpstrj first 0 last -1 step 1 filebonds 1 autobonds 1 waitfor all -mol delrep 0 top -mol representation VDW 0.500000 37.000000 -mol color Name -mol selection {type 1 } -mol material perso1 -mol addrep top -mol selupdate 0 top 0 -mol colupdate 0 top 0 -mol scaleminmax top 0 0.000000 0.000000 -mol smoothrep top 0 1 -mol drawframes top 0 {now} -mol clipplane center 0 0 top {0.0 0.0 0.0} -mol clipplane color 0 0 top {0.5 0.5 0.5 } -mol clipplane normal 0 0 top {0.0 0.0 1.0} -mol clipplane status 0 0 top {0} -mol clipplane center 1 0 top {0.0 0.0 0.0} -mol clipplane color 1 0 top {0.5 0.5 0.5 } -mol clipplane normal 1 0 top {0.0 0.0 1.0} -mol clipplane status 1 0 top {0} -mol clipplane center 2 0 top {0.0 0.0 0.0} -mol clipplane color 2 0 top {0.5 0.5 0.5 } -mol clipplane normal 2 0 top {0.0 0.0 1.0} -mol clipplane status 2 0 top {0} -mol clipplane center 3 0 top {0.0 0.0 0.0} -mol clipplane color 3 0 top {0.5 0.5 0.5 } -mol clipplane normal 3 0 top {0.0 0.0 1.0} -mol clipplane status 3 0 top {0} -mol clipplane center 4 0 top {0.0 0.0 0.0} -mol clipplane color 4 0 top {0.5 0.5 0.5 } -mol clipplane normal 4 0 top {0.0 0.0 1.0} -mol clipplane status 4 0 top {0} -mol clipplane center 5 0 top {0.0 0.0 0.0} -mol clipplane color 5 0 top {0.5 0.5 0.5 } -mol clipplane normal 5 0 top {0.0 0.0 1.0} -mol clipplane status 5 0 top {0} -mol representation VDW 1.300000 37.000000 -mol color Name -mol selection {type 2 } -mol material perso1 -mol addrep top -mol selupdate 1 top 0 -mol colupdate 1 top 0 -mol scaleminmax top 1 0.000000 0.000000 -mol smoothrep top 1 1 -mol drawframes top 1 {now} -mol clipplane center 0 1 top {0.0 0.0 0.0} -mol clipplane color 0 1 top {0.5 0.5 0.5 } -mol clipplane normal 0 1 top {0.0 0.0 1.0} -mol clipplane status 0 1 top {0} -mol clipplane center 1 1 top {0.0 0.0 0.0} -mol clipplane color 1 1 top {0.5 0.5 0.5 } -mol clipplane normal 1 1 top {0.0 0.0 1.0} -mol clipplane status 1 1 top {0} -mol clipplane center 2 1 top {0.0 0.0 0.0} -mol clipplane color 2 1 top {0.5 0.5 0.5 } -mol clipplane normal 2 1 top {0.0 0.0 1.0} -mol clipplane status 2 1 top {0} -mol clipplane center 3 1 top {0.0 0.0 0.0} -mol clipplane color 3 1 top {0.5 0.5 0.5 } -mol clipplane normal 3 1 top {0.0 0.0 1.0} -mol clipplane status 3 1 top {0} -mol clipplane center 4 1 top {0.0 0.0 0.0} -mol clipplane color 4 1 top {0.5 0.5 0.5 } -mol clipplane normal 4 1 top {0.0 0.0 1.0} -mol clipplane status 4 1 top {0} -mol clipplane center 5 1 top {0.0 0.0 0.0} -mol clipplane color 5 1 top {0.5 0.5 0.5 } -mol clipplane normal 5 1 top {0.0 0.0 1.0} -mol clipplane status 5 1 top {0} -mol rename top dump.lammpstrj -set viewpoints([molinfo top]) {{{1 0 0 -0.212156} {0 1 0 -0.189823} {0 0 1 -0.078687} {0 0 0 1}} {{1 0 0 0} {0 1 0 0} {0 0 1 0} {0 0 0 1}} {{0.0104109 0 0 0} {0 0.0104109 0 0} {0 0 0.0104109 0} {0 0 0 1}} {{1 0 0 0} {0 1 0 0} {0 0 1 0} {0 0 0 1}}} -lappend viewplist [molinfo top] -set topmol [molinfo top] -# done with molecule 1 -foreach v $viewplist { - molinfo $v set {center_matrix rotate_matrix scale_matrix global_matrix} $viewpoints($v) -} -foreach v $fixedlist { - molinfo $v set fixed 1 -} -unset viewplist -unset fixedlist -mol top $topmol -unset topmol -proc vmdrestoremycolors {} { -color scale colors RWB {1.0 0.0 0.0} {1.0 1.0 1.0} {0.0 0.0 1.0} -color scale colors BWR {0.0 0.0 1.0} {1.0 1.0 1.0} {1.0 0.0 0.0} -color scale colors RGryB {1.0 0.0 0.0} {0.5 0.5 0.5} {0.0 0.0 1.0} -color scale colors BGryR {0.0 0.0 1.0} {0.5 0.5 0.5} {1.0 0.0 0.0} -color scale colors RGB {1.0 0.0 0.0} {0.0 1.0 0.0} {0.0 0.0 1.0} -color scale colors BGR {0.0 0.0 1.0} {0.0 1.0 0.0} {1.0 0.0 0.0} -color scale colors RWG {1.0 0.0 0.0} {1.0 1.0 1.0} {0.0 1.0 0.0} -color scale colors GWR {0.0 1.0 0.0} {1.0 1.0 1.0} {1.0 0.0 0.0} -color scale colors GWB {0.0 1.0 0.0} {1.0 1.0 1.0} {0.0 0.0 1.0} -color scale colors BWG {0.0 0.0 1.0} {1.0 1.0 1.0} {0.0 1.0 0.0} -color scale colors BlkW {0.0 0.0 0.0} {0.5 0.5 0.5} {1.0 1.0 1.0} -color scale colors WBlk {1.0 1.0 1.0} {0.5 0.5 0.5} {0.0 0.0 0.0} -color scale colors cividis {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors viridis {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors magma {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors plasma {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors inferno {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L3 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L8 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L9 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L16 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L17 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L18 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L19 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_L20 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_C2 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_C4 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_C6 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_C7 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_I1 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_I2 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_I3 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_D11 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_D12 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors turbo {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} -color scale colors CET_R2 {0.0 0.0 0.0} {0.0 0.0 0.0} {0.0 0.0 0.0} - color scale method RWB - set colorcmds { - {color Display {BackgroundTop} black} - {color Display {BackgroundBot} blue2} - {color Display {FPS} white} - {color Name {LPA} green} - {color Name {LPB} green} - {color Name {1} green3} - {color Name {2} cyan} - {color Type {LP} green} - {color Type {DRUD} pink} - {color Type {1} pink} - {color Type {2} cyan} - {color Element {X} cyan} - {color Element {Ac} ochre} - {color Element {Ag} ochre} - {color Element {Al} ochre} - {color Element {Am} ochre} - {color Element {Ar} ochre} - {color Element {As} ochre} - {color Element {At} ochre} - {color Element {Au} ochre} - {color Element {B} ochre} - {color Element {Ba} ochre} - {color Element {Be} ochre} - {color Element {Bh} ochre} - {color Element {Bi} ochre} - {color Element {Bk} ochre} - {color Element {Br} ochre} - {color Element {Ca} ochre} - {color Element {Cd} ochre} - {color Element {Ce} ochre} - {color Element {Cf} ochre} - {color Element {Cl} ochre} - {color Element {Cm} ochre} - {color Element {Co} ochre} - {color Element {Cr} ochre} - {color Element {Cs} ochre} - {color Element {Cu} ochre} - {color Element {Db} ochre} - {color Element {Ds} ochre} - {color Element {Dy} ochre} - {color Element {Er} ochre} - {color Element {Es} ochre} - {color Element {Eu} ochre} - {color Element {F} ochre} - {color Element {Fe} ochre} - {color Element {Fm} ochre} - {color Element {Fr} ochre} - {color Element {Ga} ochre} - {color Element {Gd} ochre} - {color Element {Ge} ochre} - {color Element {He} ochre} - {color Element {Hf} ochre} - {color Element {Hg} ochre} - {color Element {Ho} ochre} - {color Element {Hs} ochre} - {color Element {I} ochre} - {color Element {In} ochre} - {color Element {Ir} ochre} - {color Element {K} ochre} - {color Element {Kr} ochre} - {color Element {La} ochre} - {color Element {Li} ochre} - {color Element {Lr} ochre} - {color Element {Lu} ochre} - {color Element {Md} ochre} - {color Element {Mg} ochre} - {color Element {Mn} ochre} - {color Element {Mo} ochre} - {color Element {Mt} ochre} - {color Element {Na} ochre} - {color Element {Nb} ochre} - {color Element {Nd} ochre} - {color Element {Ne} ochre} - {color Element {Ni} ochre} - {color Element {No} ochre} - {color Element {Np} ochre} - {color Element {Os} ochre} - {color Element {Pa} ochre} - {color Element {Pb} ochre} - {color Element {Pd} ochre} - {color Element {Pm} ochre} - {color Element {Po} ochre} - {color Element {Pr} ochre} - {color Element {Pt} ochre} - {color Element {Pu} ochre} - {color Element {Ra} ochre} - {color Element {Rb} ochre} - {color Element {Re} ochre} - {color Element {Rf} ochre} - {color Element {Rg} ochre} - {color Element {Rh} ochre} - {color Element {Rn} ochre} - {color Element {Ru} ochre} - {color Element {Sb} ochre} - {color Element {Sc} ochre} - {color Element {Se} ochre} - {color Element {Sg} ochre} - {color Element {Si} ochre} - {color Element {Sm} ochre} - {color Element {Sn} ochre} - {color Element {Sr} ochre} - {color Element {Ta} ochre} - {color Element {Tb} ochre} - {color Element {Tc} ochre} - {color Element {Te} ochre} - {color Element {Th} ochre} - {color Element {Ti} ochre} - {color Element {Tl} ochre} - {color Element {Tm} ochre} - {color Element {U} ochre} - {color Element {V} ochre} - {color Element {W} ochre} - {color Element {Xe} ochre} - {color Element {Y} ochre} - {color Element {Yb} ochre} - {color Element {Zr} ochre} - {color Resname {UNK} silver} - {color Chain {X} blue} - {color Segname {} blue} - {color Conformation {all} blue} - {color Molecule {0} blue} - {color Molecule {1} red} - {color Molecule {dump.lammpstrj} red} - {color Structure {3_10_Helix} blue} - {color Surface {Grasp} gray} - {color Labels {Springs} orange} - {color Stage {Even} gray} - {color Stage {Odd} silver} - } - foreach colcmd $colorcmds { - set val [catch {eval $colcmd}] - } - color change rgb 0 0.0 0.0 1.0 - color change rgb 2 0.3499999940395355 0.3499999940395355 0.3499999940395355 - color change rgb 3 1.0 0.5 0.0 - color change rgb 4 1.0 1.0 0.0 - color change rgb 5 0.5 0.5 0.20000000298023224 - color change rgb 6 0.6000000238418579 0.6000000238418579 0.6000000238418579 - color change rgb 7 0.0 1.0 0.0 - color change rgb 9 1.0 0.6000000238418579 0.6000000238418579 - color change rgb 10 0.20999999344348907 0.8600000143051147 0.9700000286102295 - color change rgb 11 0.6499999761581421 0.0 0.6499999761581421 - color change rgb 12 0.5 0.8999999761581421 0.4000000059604645 - color change rgb 13 0.8999999761581421 0.4000000059604645 0.699999988079071 - color change rgb 14 0.5 0.30000001192092896 0.0 - color change rgb 15 0.5 0.5 0.75 - color change rgb 17 0.8799999952316284 0.9700000286102295 0.019999999552965164 - color change rgb 18 0.550000011920929 0.8999999761581421 0.019999999552965164 - color change rgb 19 0.0 0.8999999761581421 0.03999999910593033 - color change rgb 20 0.0 1.0 0.5899999737739563 - color change rgb 21 0.0 0.8799999952316284 1.0 - color change rgb 22 0.0 0.5299999713897705 1.0 - color change rgb 23 0.019999999552965164 0.3799999952316284 0.6700000166893005 - color change rgb 24 0.009999999776482582 0.03999999910593033 0.9300000071525574 - color change rgb 25 0.27000001072883606 0.0 0.9800000190734863 - color change rgb 26 0.44999998807907104 0.0 0.8999999761581421 - color change rgb 27 0.8999999761581421 0.0 0.8999999761581421 - color change rgb 28 1.0 0.0 0.6600000262260437 - color change rgb 29 0.9800000190734863 0.0 0.23000000417232513 - color change rgb 30 0.8100000023841858 0.0 0.0 - color change rgb 31 0.8899999856948853 0.3499999940395355 0.0 - color change rgb 32 1.0 0.5899999737739563 0.5699999928474426 -} -vmdrestoremycolors -label textsize 1.0 diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-dark.png b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-dark.png deleted file mode 100644 index bbce9c5ef..000000000 Binary files a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-dark.png and /dev/null differ diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-distribution-dark.png b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-distribution-dark.png deleted file mode 100644 index 49369e155..000000000 Binary files a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-distribution-dark.png and /dev/null differ diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-distribution-light.png b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-distribution-light.png deleted file mode 100644 index 8b9ce8fd1..000000000 Binary files a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-distribution-light.png and /dev/null differ diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-light.png b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-light.png deleted file mode 100644 index 963c5f8a0..000000000 Binary files a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/bond-light.png and /dev/null differ diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_atom_position.ipynb b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_atom_position.ipynb deleted file mode 100644 index 90c523f79..000000000 --- a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_atom_position.ipynb +++ /dev/null @@ -1,153 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "9e485e34", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import sys, os, git\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "fc56a724", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "level: mdanalysis & tutorial name: mdanalysis-tutorial\n", - "data path: /home/simon/Git/LAMMPS/tutorials/docs/lammpstutorials-inputs/mdanalysis/\n" - ] - } - ], - "source": [ - "current_path = os.getcwd()\n", - "git_repo = git.Repo(current_path, search_parent_directories=True)\n", - "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", - "path_in_folder = current_path[len(git_path)+1:]\n", - "level = path_in_folder.split(\"/\")[-2]\n", - "tutorial_name = path_in_folder.split(\"/\")[-1]\n", - "print(\"level:\" , level, \"& tutorial name:\", tutorial_name)\n", - "sys.path.append(git_path + \"/docs/sphinx/source/tutorials/figures/pyplot-perso/\")\n", - "from functions import complete_panel, save_figure, set_boundaries, \\\n", - " add_subplotlabels, set_boundaries\n", - "from color_series1 import colors\n", - "path_figures = current_path[len(git_path):] + '/'\n", - "data_path = git_path + \"/docs/lammpstutorials-inputs/\" + level + \"/\"\n", - "print(\"data path: \", data_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "3305a3af", - "metadata": {}, - "outputs": [], - "source": [ - "timestep = 0.001 * 100 # actual time in ps between 2 recorded frames\n", - "position = np.loadtxt(data_path + \"position_vs_time.dat\")\n", - "frame, x, y, z = position.T\n", - "time = timestep * frame # ps" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "29b63dbd", - "metadata": {}, - "outputs": [], - "source": [ - "all_marker_size = np.linspace(12, 1, len(frame))" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "4803ba8e", - "metadata": {}, - "outputs": [], - "source": [ - "R = colors[\"mycyan\"][0] * np.linspace(1, 0, len(frame))\n", - "G = colors[\"mycyan\"][1] * np.linspace(1, 0, len(frame))\n", - "B = colors[\"mycyan\"][2] * np.linspace(1, 0, len(frame))" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "7fa14959", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "filename = \"position-atom\"\n", - "for mode, mygray in zip(['light', 'dark'], [colors[\"mylightgray\"], colors[\"mydarkgray\"]]): \n", - " fig = plt.figure(figsize=(18,9))\n", - " ax, n, l_tot, c_tot = [], 0, 2, 1\n", - " n += 1\n", - " ax.append(plt.subplot(l_tot, c_tot, n))\n", - " for x0, y0, z0, marker, R0, G0, B0 in zip(x, y, z, all_marker_size, R, G, B):\n", - " ax[-1].plot(x0, z0, 'o', color=np.array([R0, G0, B0]),\n", - " linewidth=3, markersize = marker)\n", - " complete_panel(ax[-1], r'$x$ $(\\mathrm{\\AA}$)', r'$y$ $(\\mathrm{\\AA}$)',\n", - " legend=False, axis_color=mygray) #, cancel_x=True)\n", - " set_boundaries(plt, x_ticks=np.arange(46, 58, 2), x_boundaries=(45, 57),\n", - " y_ticks=np.arange(12, 24.5, 3), y_boundaries=(12, 24))\n", - " save_figure(plt, fig, mode, git_path, path_figures, filename)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_bond_distribution.ipynb b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_bond_distribution.ipynb deleted file mode 100644 index 931e64c70..000000000 --- a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_bond_distribution.ipynb +++ /dev/null @@ -1,137 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "9e485e34", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import sys, os, git\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "fdeff4bf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "level: mdanalysis & tutorial name: mdanalysis-tutorial\n", - "data path: /home/simon/Git/LAMMPS/tutorials/docs/lammpstutorials-inputs/mdanalysis/\n" - ] - } - ], - "source": [ - "current_path = os.getcwd()\n", - "git_repo = git.Repo(current_path, search_parent_directories=True)\n", - "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", - "path_in_folder = current_path[len(git_path)+1:]\n", - "level = path_in_folder.split(\"/\")[-2]\n", - "tutorial_name = path_in_folder.split(\"/\")[-1]\n", - "print(\"level:\" , level, \"& tutorial name:\", tutorial_name)\n", - "sys.path.append(git_path + \"/docs/sphinx/source/tutorials/figures/pyplot-perso/\")\n", - "from functions import complete_panel, save_figure, set_boundaries, \\\n", - " add_subplotlabels, set_boundaries\n", - "from color_series1 import colors\n", - "path_figures = current_path[len(git_path):] + '/'\n", - "data_path = git_path + \"/docs/lammpstutorials-inputs/\" + level + \"/\"\n", - "print(\"data path: \", data_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "3305a3af", - "metadata": {}, - "outputs": [], - "source": [ - "distribution_initiale = np.loadtxt(data_path + \"starting_bond_distribution.dat\")\n", - "bond_length, distribution_initiale = distribution_initiale.T" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "6719e8e7", - "metadata": {}, - "outputs": [], - "source": [ - "distribution_finale = np.loadtxt(data_path + \"ending_bond_distribution.dat\")\n", - "bond_length, distribution_finale = distribution_finale.T" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "868fe5d2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "filename = \"bond-distribution\"\n", - "for mode, mygray in zip(['light', 'dark'], [colors[\"mylightgray\"], colors[\"mydarkgray\"]]): \n", - " fig = plt.figure(figsize=(18,5))\n", - " ax, n, l_tot, c_tot = [], 0, 1, 1\n", - " n += 1\n", - " ax.append(plt.subplot(l_tot, c_tot, n))\n", - " ax[-1].plot(bond_length, distribution_initiale/np.sum(distribution_initiale), color=colors[\"myblue\"], linewidth=3, label=\"at the start\")\n", - " ax[-1].plot(bond_length, distribution_finale/np.sum(distribution_finale), color=colors[\"myorange\"], linewidth=3, label=\"during deformation\")\n", - " complete_panel(ax[-1], r'bond length (\\AA)', r'probability', legend=True, axis_color=mygray)\n", - " set_boundaries(plt, y_ticks=np.arange(0, 0.13, 0.03))\n", - " save_figure(plt, fig, mode, git_path, path_figures, filename)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_bond_evolution.ipynb b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_bond_evolution.ipynb deleted file mode 100644 index ca6813d94..000000000 --- a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/plot_bond_evolution.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 16, - "id": "9e485e34", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import sys, os, git\n", - "from matplotlib import pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "fc56a724", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "level: mdanalysis & tutorial name: mdanalysis-tutorial\n", - "data path: /home/simon/Git/LAMMPS/tutorials/docs/lammpstutorials-inputs/mdanalysis/\n" - ] - } - ], - "source": [ - "current_path = os.getcwd()\n", - "git_repo = git.Repo(current_path, search_parent_directories=True)\n", - "git_path = git_repo.git.rev_parse(\"--show-toplevel\")\n", - "path_in_folder = current_path[len(git_path)+1:]\n", - "level = path_in_folder.split(\"/\")[-2]\n", - "tutorial_name = path_in_folder.split(\"/\")[-1]\n", - "print(\"level:\" , level, \"& tutorial name:\", tutorial_name)\n", - "sys.path.append(git_path + \"/docs/sphinx/source/tutorials/figures/pyplot-perso/\")\n", - "from functions import complete_panel, save_figure, set_boundaries, \\\n", - " add_subplotlabels, set_boundaries\n", - "from color_series1 import colors\n", - "path_figures = current_path[len(git_path):] + '/'\n", - "data_path = git_path + \"/docs/lammpstutorials-inputs/\" + level + \"/\"\n", - "print(\"data path: \", data_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "3305a3af", - "metadata": {}, - "outputs": [], - "source": [ - "timestep = 0.0005 * 1000 # actual time in ps between 2 recorded frames\n", - "number = np.loadtxt(data_path + \"number_bond_vs_time.dat\")\n", - "frame, number = number.T\n", - "time = timestep * frame # ps" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "6719e8e7", - "metadata": {}, - "outputs": [], - "source": [ - "length = np.loadtxt(data_path + \"length_bond_vs_time.dat\")\n", - "_, length = length.T" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "868fe5d2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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qVq9X1upiU4sWW6L18bnTdOt0i0KCglRW7dGmX76jspr6PvcujI3Sf3/+dn1shlXrbDO18fARtXR29fs898yO0/98/g7NjoqY6Jd0Q7HF+gZ/FQ0tAaoEAAAMZcoEf93S09OVkpKie+65h44/KCsrS0VFRSouLvY573A4JEkpKSnau3evzzzI3qGxy+XSnj175Ha7tWHDBmZHAgAAAAAwRoZh6LcXq7X/1AW9Vlmj47X1Mvq5LjY0RHfOsqr4Uo1a+wnzfm/BbP3Hp25RXHiYJOmz82eqYP2d2nD4iJp7dfp9Y5lNT6UmKTw4eCJe0g2tT8ffIPMUAQBAYE254E+61umVlpamAwcOBLoUTAIHDx5UQUGB8vLy5HK55HQ6ZbFYlJGRoezs7D7XX7hwIQBVAgAAAABw/TMMQz+vuKJ/eOeEXqusHfL6+vYO/fL81T7nI4KD9P17Vipr5QKZTCafxz6dMEOF6+/U7x0+ovr2DoUEmfTP9yTqkcS+12J8sNUnAABTx5QM/iRpwYIFPvP+cGPr3goWAAAAAAD4n2EYeqG8Ut9755TevlI3prWSp5m1/zO3aWW8ecBrPjlvuk5s+pSKLlVr9UyrFpqjx/ScGFzvrT4vNbWqtbOT7koAACahKRv8paene2e2AQAAAAAAIDDevVKnLcUOvX558A6/sKAgfWyGRTdbovVWVZ0+rGvoc81fJN+knXcuV0TI0IHSnOgIfeXmeaOuG8Nni4nqc+5CY4tuInAFAGDSmbLBn81mk81mC3QZAAAAAAAAN6TqljZ9+80PtPe4s9/5fZJkj41U1soFSp0zTbfOsPh0iFU2tei3F6tVdLFa9e0d+voymz4xb7p/iseIWMJCFBsaovr2Du85V30zwR8AAJPQlA3+AAAAAAAA4D9N7R0639ii8w3Neu+qWzvfPaWa1vZ+r73ZEq0dty1W5uIEhQYH9XvN7KhrHXt07U1+JpNJtphIHaut955zNTQHsCIAADCQUQd/mzdv1vnz5/Xcc88pJiZmPGsaVw899JBcLpcOHToU6FIAAAAAAACmBMMw9GFdgw45L+uliiq9d9Wj6pa2Ie9bZI7S39+xTF+5ea5CgvoP/DA12WIJ/gAAmApGHfylpKRo+/btuuuuu/Tcc89p5cqV41nXmFVUVOi+++6Ty+XS448/HuhyAAAAAAAAJo32zi49WXZO/33mompb2zU9IkwzIsM0PSJMkvTL81dVXt807PWiQoL17Y8t0bdW3eSznSeuH7aYSJ9jVz3BHwAAk9Gog7/MzEx5PB7t3LlTa9euVXZ2th566KHxrG3UnnnmGeXk5EiSHn/8cWVkZAS4IgAAAAAAgMnhvStufeOV9/XeVbf33IkxrPflRXP1/XtWan6vYAjXlz7BX8Pwg2EAAOA/Y5rxl5WVpdraWj399NPKycnRiy++qN27dwes+6+kpETbtm2Ty+WSYRjatWsXoR8AAAAAAICklo5Offftk9r9/ml1Gsao17GEhSghOlLL42L08MoF+lTCjHGsEpOVLbZ38EfHHwAAk9GYgj9Jys7Olt1u1/bt21VaWqq1a9cqNTVVO3bs8FsAeOjQIe3Zs0cOh0PG735w3bdvn9avX++X5wcAAAAAAJhMWjo6VVZTr7OeRp3xNOmMu1GvXLyqM56RdWnNi47QetssfX7+DC2Pi1VCTITMYaETVDUmM1tMlM+xq75ZhmHIZDIFqCIAANCfMQd/0rVtP202m+6//35JUlFRkYqKimSz2fTAAw8oLS1N8+fPH4+nkiTV19eruLhYL7zwggoLC73nDcOQ3W7X3r17lZiYOG7PBwAAAAAAMBXUt3XoH4+e1j8dPav69o4hr0+Mj9VfrVqkxo5OXWlu05WWVjW0d2p5XIzW22YpMT6WYAeSpPkxET7HjR2dqm1tV/zv5kICAIDJYVyCP0lKTU3Vq6++qs2bN6usrEwmk0kul0s5OTneeXspKSlKSkrSqlWrlJSUJKvVqtjY2AHXrK+vV11dnZxOp8rKylRUVKSjR4/K4/F4rzF6bE2RmZmpxx9/fLxeEgAAAAAAwJTQ1tmlf//Aqe++fUJVzW1DXh8aZNKO2xZrx21LFBYc5IcKMdXNi46USVLPTWJdDc0EfwAATDLjFvxJkt1u10svvaS8vDxt375dkmQymbzhXHFxsYqLi/u912w2y2q1qq6uzifY6617rZ7rpqSkKDs7my4/AAAAAABw3Wvp6NSFxhadb2zW+YYWuRqa9cMPnMPexvOOmVb98BO3KGmaeYIrxfUkLDhIc6IjdLGxxXvOVd+sW6ZbAlgVAADobVyDv26ZmZnauHGj8vLy9NRTT8ntdvs8bvQzQNrtdve5rqfubSW6Az/DMJSWlqZvfvObBH4AAAAAAOC6ZhiGii5Va/d7p/Xziivq7Oe9ld5MkuyxUVpkjtJN5mgtskTptukWfWreDAUHsX0nRs4WE+kb/DU0B7AaAADQnwkJ/qRrHXxZWVnKyspSQUGBXnzxRZWUlPiEe8PZI747JOz+a1JSkjZu3Ki0tDTZbLaJKR4AAAAAAGAS6DIM/excpXa9d1pvVtUO+74N9lnaeedyJdLVh3Fki4nUG5c/+vfQ1TC8LlMAAOA/Exb89ZSenq709HRJUllZmYqLi1VUVCSXy6W6uroBO/0sFouSk5O9cwFTUlJkNvMDKwAAAAAAuL6Ue5r0ZNlZHblcp07DUKdhqMuQqppbR9RVddesOO26a4VS506bwGpxo7LFRPoc0/EHAMDk45fgr6fExEQlJibq4Ycf9jnv8XhUV1cnSbJarZM64HO73dqzZ4/y8/P1+uuvy2KZ2L3M3W63XnzxRe/fl7i4OJ/He/696jkfsba21nuclJQku90ekPUBAAAAAMDAfu6q0qZfvqPa1vYR3RcTGqz5MZFKiI6ULSZSGxfM1oYFs4a1wxIwGrZY3+CvguAPAIBJx+/B30DMZvOkDvskyel06qmnnlJ+fr73XF1d3YQHf0ePHtW2bdvGtEZ2draysrICsj4AAAAAAOjLMAzteu+0drz5gYae2HeNPTZSf7XqZmUsmae48LAJrQ/orU/HXz3BHwAAk82kCf4mM4fDoSeffFKFhYWBLgUAAAAAAFwH6ts69PXfvKefnr00rOuT4mO17dbF+vKiuQoNDprg6oD+2WKifI4vNrWovbOLfycBAJhECP4G4Ha7lZ+fr7y8PDmdzkCXAwAAAAAAprguw9C7V9wqcFYq7+R5nfE09bnmCwtna51tpoJNJgWZTAo2mbTYEq27ZsWxhScCrvdWn13GtfDPHhs1wB0AAMDfCP56cbvd2rx5s4qLi33OZ2Rk6JFHHtG6devkdrv9WlPPmXtZWVne5++eiSj5zt7r/Zgk2Wy2gK0PAAAAAMCN4GRdg/7NcVYvuarU2tmlmZHhmhkZrhmR17bkfLniii43t/Z7r0nSzjuXa9utNxPwYdKKDw9VVEiwmjo6vedc9c0EfwAATCIEf71YLBZv6GexWLRlyxZlZGR45/hZrVa/B389a8vOzp6y6wMAAAAAcL0xDEOvXKzWPx89owLnZZ9ZfecbW4a1Rlx4qA585mP6vG3mxBQJjBOTySRbTKQ+rGvwnnM1MOcPAIDJhOCvH2lpaUpNTVVmZmafx8xmcwAqusZqtU7p9QEAAAAAuJ68XlmjrOJSvX/VM/TFA7htukX//fnbdZM5ehwrAyaOLZbgDwCAyYzgrx/79u0LdAkAAAAAAGAS+58zF5Xxq3fV2tk14nuXWKKVvmCW0myz9Il50xXE1p6YQmwxvnP+XPV9Z1UCAIDAIfibQia62zCQ3YwAAAAAAEwVexzn9GclDp9tPbtZwkL04Aq71syepqstbbrS0qqq5lbVt3VqRVyM0uyztNga4/eagfHSJ/ij4w8AgEmF4G8KYatPAAAAAAACxzAMffvIh9r57qk+jy0yR+nPkxfpa8vmKyaUt1tw/SL4AwBgcuMn0SmgtrZ2Sq8PAAAAAMBk1tll6IynUV2GobjwUMWFhyksOEiSVN/WoTOeRp1xN+qnZy/p4OkLfe7fvMKuPSlJCgkK8nfpgN/ZYqN8jgn+AACYXAj+phC2+gQAAAAAYPw0tnfohx+49I9Hz/QJL6JDghUeHKSa1vZB1/juHUv17Y8tkYk5fbhB9O7487R1yN3aLkt4aIAqAgAAPRH8TSE9t+J0Op3Ky8tTcXGxXC6X3G63LBaLbDabNm7cqLS0NNnt9km1PgAAAAAAk8HV5lbtKTunPWXlqm5p6/eaxo5ONXZ0DrhGkEnam7pKf7KC341xY0mIiehz7lhtve6ZHR+AagAAQG8Ef1OM0+nUtm3bVFxc3Ocxt9sth8Mhh8OhnJwcZWRkaPfu3ZNqfQAAAAAA/KWmpU3/ePSMjtfWq6alXdUtbappbdOVljZ1dBmjXjcyJFjPffZj2rBg9jhWC0wN4cHButkSrdPuRu+5H37gIvgDAGCSIPibAjwejySpoKBA+fn5w74vPz9fpaWleu6552SxWAK2fm/Hjx8f9rWSNG/ePM2bN29E9wAAAAAAbmyXGlv0qRde04d1DWNeyyQpISZSi8xRSp5m1iOJC7XEGjP2IoEp6qtLEvS3b53wHu8/dV7fv2eF4sLDAlgVAABTw4ULF3ThQt+50QMZaaZC8DeFuN1uSVJSUpI2btwom80ms9ksj8ejoqIiFRQUeK/p5nA4tHnzZh08eDDg63fLzs4e9rWS9K1vfUt/+Zd/OaJ7AAAAAABTT0dXl07UNeh4bYNmRITpntnxCgsOGvE65xua9akXXtOpHh1Jg1lvm6ltty7WXbPiVNvaptrWdtW2tquxo1MJ0RFaEBuliJDgEdcBXK/+dIVd333npLdztqWzS//xYYW+tWpRgCsDAGDyO3jwoP7pn/5pwtafssFffn6+cnNz9eqrrwa6lAlXV1fn/XrXrl3KzMzsc016erp2796tnJwc5ebm+jxWXFysvLy8fu/zx/oAAAAAAPSnuqVNPztXqdcv1+j9qx45ajxq7ezyPj4/JlLbbr1ZX19mU+QwgzdXfZM++cJrOutpGvS6kCCTNt08T4/ecrOSppm952dFRWhWVN8ZZgA+MjsqQn9w0xw9d/qi99zTx8r158k3KchkCmBlAABgygZ/TqdTLpcr0GX4jcVi0XPPPaekpKRBr8vOzlZcXJxycnJ8zu/cuXPQYG6i1wcAAAAAQJJqW6+Ffc+dvqhfXrgy6Ky9ioZmbSl26HvvnNRfrVqkzSsXKCZ04Lcyznka9akXXld5vW/ot9gSrW8mLdS08DDFR4RpWkSoFltiZA0PHbfXBdxoHlm50Cf4O+1u1C8qrujztpkBrAoAAJgMwxj9NOsA2rZtmwoKCnTs2DG/Pu/atWvlcDi8x6+99prsdrtfaxiOe+65R06n0+fceNY6kvWPHDmiL37xi97jnJwcrVixYtjPxYw/AAAAAJj6KhqatfX14/qfsxfVPkjYNxhLWIi+tGiuMpckKGXONG9n0YnaBv332Yt6+li5Lja2+NyzzBqjX228R3Oj6eIDxpNhGEr+r1dUVlPvPbdxwWz9bN3qAFYFAMDkN5oZfz1HqD3//PNavXrg77cB7/irr69XXV1dn9lxgyktLVV+fr5MbB0woMcff1ybNm3yOVdcXDxuwd9Y1l+xYsWg/1ICAAAAAK4fnV2Gco+d0443P1BDe+ew7pkWEabqlrY+591tHfrBBy794AOX5sdEap1tpl6rrPEJHnpaGRerX228m607gQlgMpn0SOJCPVxU6j1X4KyUs75J9tioAFYGAMDkNtHNTgEJ/srKyrRnzx4VFxfL4/EEooTrXmpqqux2u09XXu8Ovcm8PgAAAABg6nNUe/SnrxzVm1W1A14TERykdbaZund2vG6ZbtGqaWZNjwxX0cVqfe+dk/rF+Sv93lfR0Kx9xwf+PTR5mlm/3HC3ZkSGj/l1AOhfxuIEbX39uOrbOyRJXYa095hTO+9aHuDKAAC4cfk9+Nu+fbvy8/MlXdsSABMnMTFxQsO4iV4fAAAAADC1GIahD+saVHSxWr+9WK3/Pnux3xl+4b8L+768aK42LJjd79y+1LnT9PLcu3Xkcq1y3j2lF8orh11H6pxp+t+1d2haRNiYXg+AwcWGheirSxP0VFm599wPPnDq7+5YovDg4MAVBgDADcyvwd/27duVl5fnPe69VSdB4Pjqve3meM8inOj1AQAAAACTU01Lm8pq6nW+oVkXGlt0vrFZ5zxNeuNyra70s0VnT3+y3KZdd61Q/DBDudWz4vSzdavlqm/SgdMXlHfyfL9be86JCtcf3DRXf7hojs/8PwATK2vlQp/g70pLm3565pIyliQErigAAG5gfgv+XC6X8vLyfMK+7qDPYrHIZrPJarX6q5wbQlxcnM+xzWabUusDAAAAACaX6pY2/e2RD7XvA2e/nXyDWWKJ1r5PrNLH504f1XPbYqO07dbF2nbrYpVWu7X/1AWd9TRpblSE/uCmObp3TjxhHxAAK+Jj9Ym50/TKxWrvuWeOlRP8AQAQIH4L/np2+knXQr+MjAxlZ2fLbDb7q4wbSm2t7wyF1NTUKbU+AAAAAGBy6OwytO94ub595EPVtLaP6N6QIJO23XKzvv2xJYoIGZ+t/5KnWZQ8zTIuawEYu0cSF/oEf69frlWXYRDGAwAQAH4L/oqLi2UymWQYhkwmkw4cOKCUlBR/Pf0Nye12e79OS0ubcusDAAAAACZOW2eXCp2X5axvUn17hxraO9XQ3qGmjk5FhQTLHBYic1iookKC9R8funS02jPstedFRyh1zjSlzp2mNPsszY+JnMBXAiDQ7php9TnuNAw1tncqNsyvU4YAAID8vNWndG2uX1ZWFqHfEJxOpzZt2qTDhw/LYhndpxhLSkq8X3/zm9/06/oAAAAAgMmrtNqtDYeOyNXQPOo1QoNMuskcrYToCCXERCohOkJLrDFKmROvBbFRPqM+AFzfzP0EfJ62doI/AAACwG/ffbu7w0wmkzIzM/31tJOW2+1WcXGxzGZzv1tkWq1WOZ1O5eTkaPfu3SNe3+l0yul0SpIyMjKUlJTk1/UBAAAAAJPTyxVV+sOfv6369o5Rr/HlRXP1xN0rZIuNGsfKAExVsaH9BH/tHZoXgFoAALjRBfnriXp2lc2fP99fTzvuPJ7hb20yEKfTqbvvvlubN2/Wpk2bdN999/W5xmKxKC0tTfn5+d6AbSS2bdsmSbLb7crOzvb7+gAAAACAyedHH7iUdujNUYd+ifGx+s3Ge/Tc524n9APgFRIUpKheMzw9baP/cAEAABg9vwV/ycnJ/nqqCVVXV+dzPJogMCcnx2c+XnFxsfLy8vpc190Z2R2yDVdubq6Ki4tlsVgG3cpzotcHAAAAAEwO7tZ2/c2RD/WNV95XR5fh89gya4x+b8FsZSyep4dW2PUXyTdp8wq77l88T+n2Wfr43GlaZ5upZ1KT9d6XPq5PzJseoFcBYDLrvd2np609QJUAAHBj89tWnw8//LCKi4slSRUVFVOy66+oqMgnsJOkZ599dsRbZXbPO+ypv6671NRUWSwWFRcXKycnZ1iddTk5OcrNzZXdbteBAwcGDeUmen0AAAAAgP8YhiFnfbPer3br/atufVjXoLOeJp1xN6qmtf834L+8aK7+81O3KqJXpw4AjJQ5NESVavUe0/EHAEBg+C34S01N1fr163X48GEVFhbqoYceGtN6+fn52rlzp44dOzZOFfpyOBwqLi5WbW2t3G63XC6XN7jsXUdJSYkSExNlt9sVFxenxMTEfuf2ddu4caMcDkefc/1JT09Xfn6+cnNzlZ+fr4yMDKWkpMhut0u61nHodDpVVFSkgoICud1uZWRkDDuMnOj1AQAAAACj83TZOX33nZOaFh6mb61apD9aOl/BQSafa+pa2/XMsXK9VFGl96+65R7BG+1bb7lZj921XEEm09AXA8AQzGGhPscj+f8RAAAYP34L/iRp3759Wrt2rZ588kllZmYqJiZm1Gt5PJ5xmbc3kO4uuOFwOp0+HXtpaWmDBn9ZWVmqra1Vfn6+rFarduzYoaSkpH6vfeCBB5Sfny9Jcrvdys3NVW5ubr/XpqSkKDs7e8C1ArE+AAAAAGDkXjhXqaziax8YrWxq1TdeeV//XHpGu+9aobW2mapuadO/lJ7Vk2XnRtxVE2SSnkpJ1kMrF0xA5QBuVGz1CQDA5ODX4E+SnnvuOa1bt06bN2/2Bk6jUV5eLrPZPI6V+crKylJWVtaErZ+dnT2srTWTkpJ0/Phx5efnq6ioSC6XS3V1dXK73bLb7bLZbEpNTVVaWpq3S28kJnp9AAAAAMDIVDQ0649/816f82U19Vp/6E2tnmnVsZp6NXZ0jmhdk6TbZ1qVs3qZPjt/5jhVCwDX9An+2un4AwAgEMYU/NXX14/4nqCgIG/nX1pamnbv3i2bzTaiNY4ePaqCgoIRP/dUZbFYJjSInOj1AQAAAADD09HVpYxfvjPgTD5JOlJVN+gaceGhunW6RcnTzFpsidZN5mgtMkfJHhulsOCgca4YAK4xh/bu+CP4AwAgEEYd/CUkJMg0xjkAR48e1dq1a0d1r2EYY35+AAAAAAAmk++9c1LFl2p8zsWEBquhfeDuPpOkP1w0VxmL5+nW6RbNj4nk92UAfmcJ953xR/AHAEBgjDr4W7NmjUpKSkb9xN2/hBiGMeo1AAAAAAC4Xrxy4ar+4Z2TPufssZF69w8/rhfKK/XtIx/qQmOL97Egk3T/4gTtuG2xlsfF+rtcAPDRp+OvnRl/AAAEwqiDvw0bNqikpGTMnyIc7f0EhgAAAACAqaClo1On3I26yRyl6ND+fw2/1NiijF+9q64ev+oGm0w6+JmPKT4iTF9bZtOXF81V7rFyHXZVaUVcrP5f8kLdbInx06sAgMH1mfFHxx8AAAExpuBv27Ztkq6FcBaLRVardbzq6lddXZ0kye12T+jzAAAAAAAwVsdqPNp73KlnT5xXXVu7woOD9Ol50/V7C2drg3224iNCVei8rGdPnleh87Lau3w/4Jpz5zLdNTveexwVGqK/uuVm/dUtN/v7pQDAkMxhbPUJAMBkMOrgz2w2y2KxyOPx6Pjx4zKbzeNZ16DcbrfWr18vl8vlt+cEAAAAAGAoLR2d+unZi9p7zKmSSt9Zfa2dXTrkqtIhV5U2q3TQ2X2fTZihRwn4AEwhfbb6bGOrTwAAAmHUwZ90bc5fRUWFX0M/SbJYLFqzZo3279/v1+cFAAAAAKA/V5pb9cyxcu0pO6eq5rZh3TNQ6Dc7Klw/+fStChrjaA0A8Kc+W3220/EHAEAgjCn4s9vtqqioGK9aRmTBggUBeV4AAAAAALp9WFuvfy49q5+cqFBLZ9eY1goPDtIXFs7W9+9eqdlREeNUIQD4B1t9AgAwOYwp+EtJSRmvOkYsMTFRa9asCdjzAwAAAABuTIZh6DcXruqfSs+q0Hl50GtnRYbrG8tt2nTzPDlqPPpZeaUOu6p83hD/+NxpemBJgv7gprmyhocOshoATF59Ov7aOmQYhkx0LwMA4FdjDv5GGv6tXLlSt9xyix5++OFRB3dlZWW65ZZbdODAgVHdDwAAAADASLV1dum50xf0T6Vn9P5Vz6DXfnLudGUlLtDGBbMVFhwkSUqcZtamxQlq6+xSyaVqNbR3atV0s+yxUf4oHwAmVO/gr9Mw1NTRqejQMb39CAAARsjv33ndbreKioqUkpIy6uDvySef1KuvvqqXXnpJCQkJ41whAAAAAOBGdqquQXuPO/X65Rq5Wzvkae+Qp61d9e0d6jIGvi80yKRNN8/TX6xapFumWwa8Liw4SJ9KmDEBlQNA4Jj7Cfg8bR0EfwAA+Jnfv/NaLBZ5PIN/MnIoW7ZsUWFhodauXavnnntOK1euHKfqAAAAAAA3oi7D0M9dVXqy7JwOu6pGdG9ceKgeXrlAjyQu1NxoZvMBuDH1nvEnXQv+5kQHoBgAAG5gU/IjN0lJSZKudQ9u3bpVhYWFAa4IAAAAADBVdHR1yVnfrJN1DTrpbtTJugb94vwVnXI3jmidmy3R+ovkm/RHS+fT0QLghhcWHKSI4CC1dHZ5z3na2wNYEQAANya//2ZitVrH3PHXU3l5+bitBQAAAAC4fhiGofeuuvVWVZ1O/S7gO+lu1FlPo9oH27NzCClz4vWXqxYp3T5bwUGmcawYAKY2c1iIWprbvMeeto4AVgMAwI3J78Gf2Wwe8xpPP/20pGu/xI1niAgAAAAAuH48+vpx/ePRM6O61xoWqm8st+n2GVbFhoXIHBoic1iIZkeFa1YU23kCQH/MYaGqIvgDACCgxjX427lzp55++mnZbDZZLBbZbDZZrVZJ12b7SZLL5ZIkvfDCC6qtrR3R+m63W6WlpXI4HDKZTDIMQzabbTxfAgAAAADgOvCD485RhX6J8bH6ZtJCZSxOYPtOABghc6//b3ra2OoTAAB/G9ffYjZs2KDc3Fy5XC4ZhiGHw9Hvdd2PDfT4YAzjo+1YTCaTd94fAAAAAACS9HpljR4pHvr3zbjwUC21xmixJVpLrDH6+JxpWjMnXiYT23cCwGiYw3oFf+10/AEA4G/jGvwlJSUpJSVFJSUlE/aLUve63QHgli1bJuR5AAAAAABTz8XGFv3Bz99SW1eXz/kvLJytFXGxWvK7kG+JNUbTIsICVCUAXJ/MYaE+x2z1CQCA/437viUZGRkqLi4e8rqenXujYbFYtHv3biUmJo5pHQAAAADA1NLR1aXa1nY1tHdobnSEwoODJUmtnZ36w5+/pUtNrT7X/+WqRfr+PSsDUSoA3FD6dPwR/AEA4HfjHvylp6crOztbZrPZO9/PbDZ7H9++fbtcLpdSUlKUlpY24vWtVqvsdjuBHwAAAADcAAzD0E9OVOjHJyrkrG9WTWub3D3eSA42mbTIHKUV8bFqbO/U65d9Z8l/JmG6Hr9rub/LBoAbUu8Zf25m/AEA4HcTMqn84YcfHvSx7du3KykpSRkZGRPx9AAAAACA64CrvknfeOV9/fL81QGv6TQMnXQ36qS7sc9jC2OjdPCztyskKGgiywQA/A4dfwAABJ7ff/vJzMyUxWKR2+3291MDAAAAAKYAwzD0ow9cSvqvVwYN/QYTFRKs59fewRw/APCjPjP+2gn+AADwtwnp+BvK/fffr4qKikA8NQAAAABgkqppadNrlTXKPVauw66qMa31H5+8RaumW8apMgDAcFj6dPyx1ScAAP4WkOAvOzs7EE8LAAAAAJgkalra9GFdgz6sbdCRqlqVXKrRsdr6Aa+fFx2hnNXLtNAcrfjwUE2LCFN4cJBOuxt1vLZex2rqdby2Xh2GoT9ZbteXFs3146sBAEhs9QkAwGTg9+Bv//792rBhg2JjY/391JgkvvGNbygsrO92Ow8++KA2b94cgIoAAAAATKTOLkOvX67RC+WVeuNyrT6sbdCVlrZh3/+1pfP1z/cmyhoe2uex1RFhWj0rbjzLBQCMkjm011afBH8AAAzb3r17tW/fvj7n29qG/7uTFIDg79lnn9W2bdu0d+9erV+/ftzWraiokNPplNVqVWJi4riti/FXU1PT7/mGhgY/VwIAAABgojR3dOrnFVX62blKFTgv6+oIgr5us6PCte/jq7RhwewJqBAAMN76dPwx4w8AgGFraGhQZWXlmNcJyFafhmGoqKhoXIK/xx57TLm5uX3Op6en65FHHiEEnITi4+P77fiLiYkJQDUAAAAAxothGHr7Sp1+9IFLB05fkHsUnR6RIcG6c6ZVn0mYoYdXLlB8RN/fHQAAk1PfrT7bZRiGTCZTgCoCAGDqiImJ0ezZfT/02NbWNmBDVX/8HvxZrdZx+2Z/7733yuVyyTCMPo8VFBSooKBAmZmZeuyxx8bl+TA+fvjDH2r16tWBLgMAAADAOKlv69APPnDqRx+6VFYz8Jy+3kKDTFpsidHyuBjdPStOa+ZM063TLQoLDprAagEAE6V38NfeZai1s0sRIcEBqggAgKlj8+bN/Y5DO3LkiL74xS8Oe52AdPyNh8cee0xOp1OSvEFizwCw++u8vDy5XC7l5+f7v0gAAAAAuM69UVmjr/ziHbkamoe8Nik+VhsXzNads+K0zBqjheYohQQR8gHA9aL3jD/p2pw/gj8AAPzH78Gf2WyWJNXV1enQoUM6evSoJMlut2vDhg2KjY0d1jpPPfWUT+egYRhKSUlRamqqzGazSktLVVBQILfbraKiIj388MN6+umnx/8FAQAAAMB17FiNR+c8TbprVpymR4Z7zxuGoX8uPattbxxXR1ffXVgkySTp43On6QsL52jjgllaaI72U9UAgEDo3fEnSe62ds2MCu/nagAAMBECNuOvsLBQhYWFPue3bds2rK05XS6Xz1omk0m7du1SRkaG93xGRoZ27dql3Nxc7dy507vt57333ju+LwYAAAAArjOtnZ366ZlLeqrsnF6/XCtJCjJJKXOm6QsLZuuT86brb458qBedl/u9f2FslP542Xz90dL5ssVG+bN0AEAAhQcHKTTIpPYeHwjxtI983isAABg9vwd/3aFdf3P5pGtbc5aWlvYJBXvq3uJTurbNZ1pamk/o11NWVpZsNpseeughbd68WWVlZWOoHgAAAACuX2c9jfrRBy79+wdOVTW3+TzWZUi/vVit316sHvD+lDnx+vs7lunjc6cpaJxmuwMApg6TySRzWKiqWz76HuJpI/gDAMCf/DpMIT8/Xw6HQyaTyfunN8MwVFpaqocffnjI9brDw40bNw56XXp6utavXy+3263Dhw+PrngAAAAAuM50dhl6rbJGf/3GcSUe/I0W5f9KOe+e6hP6DcUk6dsfW6Jfb7xHn5w3ndAPAG5gll7bfXra2gNUCQAANya/dvzt3LnTG/YZhiGLxaI1a9bIbrfLbrertLRUpaWlcjgcKigo0JYtW7Ry5coh101KShrymuzsbB06dEh5eXlat27dmF8LAAAAAExVrZ2d2v3eaT3pOKcrLSML+XqbERGm/M/cps/OnzlO1QEAprLec/7o+AMAwL/8FvyVlZXJ7XbLZDLJMAxlZ2f36err3q4zLy9P27dv19atWwfd8rOb1Wod8hq73a77779fBw4cGFX9AAAAAHA9eKOyRt945aiO19YPee0n5k7TlsSF+uz8GXrlQrX+r/ySXii/7N3C7ZNzpyv/M7dpTnTERJcNAJgizKGhPsfM+AMAwL/8Fvz1nMt34MABpaSkDHhtZmamPB6PHnvsMb366qu69957B107NjZ2WDWkp6frwIEDKisrU2Ji4vAKBwAAAIDrQGN7h/7myIf6l9Kz6n/i+jXTI8L0hzfN0SOJC5U4zew9v3HhbG1cOFsdXV16/6pHhgzdPsPa7wgHAMCNi44/AAACy2/B39GjRyVJWVlZg4Z+3bKysvTUU0+poKBgyOBvuG655RYZhiGXy0XwBwAAAOC6d7mpRW9ertMbl2t18PQFnatv6ve6xPhYpdtnacOC2bpzZpyCgwYO80KCgnT7TOsEVQwAmOr6Bn/M+AMAwJ/8OuPPZDINK/TrlpycrKKionF7frP52qdV6+rqxm1NAAAAAAg0wzDkrG9WabVHjhqPSqs9equqbsCgr9tt0y3690+s0m0zrP4pFABw3TOH0vEHAEAg+S34s9vtkqRVq1YN+x6z2SyXyzVuNXg8Hp+/AgAAAMBU425t94Z7jmqPSms8clTXq34EM5TCg4P0nduX6q9uWaSQoKAJrBYAcKMxhzHjDwCAQPJb8JecnCzpWrfdcGfylZWV9Xu+d3BXX18/rDW7Q8Tuzj8AAAAAmCpO1TXoL187pgLn5UFn9A3l3tnx+uEnbtHSuJhxqw0AgG7M+AMAILD8FvwlJSXJMAyVlZVp/vz5Q17vcrnkdDplMpn06quv+sz5690FONwwsXs9q9U64voBAAAAIBCaOzr1+Lun9Ph7p9XW1TXi+6NDgnXHTKvumhWnT86brs8kzFCQaeAZfgAAjEXv4M/NjD8AAPzKrzP+7r//fn3ve9/TunXrhrx28+bNkq7Nqrjvvvt08OBBb/iXl5cnk8kkw7j2Odfhhol79uyRdC2EBAAAAIDJwt3arp+VV+p8Q7OmRYRpekSYZkSG62pLqx59/bjOegaf1dfNGhaq5Glm75/VM61aGR/Ldp4AAL9hxh8AAIHl1+Dvq1/9qvbv36+0tDTt3r1bK1eu7HNNWVmZtm7dKofDIZPJpIcffljl5eW67777lJSUJKfTKbfbLZPJ5A3/cnJyhgwTCwsL5XA4ZLFYhhUSAgAAAMBEq2lp07+WntW/Oc6pbgQdESFBJi2zxngDvqT4a3+dFx0hE918AIAA6jPjj44/AAD8yq/BX2JiotasWaOSkhKtXbtWNptNSUlJslqtqqurk8Ph6LONZ2pqqnbs2KF77rlHDofD2+VnGIZMJpPS0tJUWFiohx9+WE8//XS/z1tSUqKtW7fKZDJpw4YNE/46AQAAAGAwFxqa9WTZOT1Vdk4N7Z3Dvi/YZNL/S16ov7t9aZ83VgEAmAwszPgDACCg/Br8SdLevXt19913q76+Xk6n0yfo6w71urv5kpKStGbNGknSgQMHtH79enk8Hp9rn3jiCVksFu3fv18Oh0OPPPKI1qxZI6vVKqfTqby8POXn53uDwkceecTPrxgAAADAjcowDB2rqVdJZY3Kajw6VlOvYzX1utLSNuK17p0dr6dTk5U0zTwBlQIAMD76dPy1E/wBAOBPfg/+LBaLnnvuOd13331yu90+j3VvSdMd0u3du9f7mN1u18GDB7V582ZvWPj444/LbDbr29/+tvbv3y+n06mtW7f2ec7u9bKystjmEwAAAMCEau7o1G8uXFWh87IKXZflrG8e9r0pc+LV1mnoakurrrS0ydPWoeVxMXr0lpv1R0vnK4htPAEAk5y5V8dfa2eXWjs7FR4cHKCKAAC4sfg9+JOkpKQkHT58WN/73vd06NChPo+npKRo165dfUK6pKQkvfbaa3K5XLJarTKbr33S1Ww2e7f67J771607TExJSdFf//VfT+CrAgAAAHAjO+1u0OPvntb+0xfU3DH87TuDTNJ9N8/TjtsWa2W8bzdfZ5eh4CDCPgDA1NE7+JOk+rYOhUcS/AEA4A8BCf4kyWazad++ffJ4PDp69KhcLpdsNptWrVrlDfQGu7e37OxsORwOlZSU+Jw3DEOPPPIIoR8AAACACfFBbb12vntK+0+dV5cx9PXdokKC9eVFc/XXty3WEmtMv9cQ+gEAphpzaN+3Gz3tHZoeGR6AagAAuPEELPjrZjablZKSMi5rHTx4UC6XSw6HwxskpqSkDBkkAgAAAMBwuVvb5ajxqLTao1+dv6rnz13SUHnf/JhI3Ts7XivjY7UyLlaJ8bG6yRxNsAcAuO5EhgQr2GRSZ48duTxtzPkDAMBfAh78jTebzdZvRyAAAAAAjIZhGHqtska5x8r1WmWtyuubhrwnyCTdMyteafZZSrPPUmJ8rHcMAQAA1zOTySRzWIhqW9u95wj+AADwnykb/OXn5ys3N1evvvpqoEsBAAAAcB3q7DL0f+cu6ftHz+iNy7XDuic6JFhZiQv0rVWLNDsqYoIrBABgcuob/LUPcjUAABhPUzb4czqdcrlcgS4DAAAAwHWitbNTp+oa9UFdg8qqPco/dV5nPEN390nX3uD8s6Sb9OfJN2laRNgEVwoAwOTWe86fm44/AAD8ZsoGf263O2Cz+9xut/bs2aP8/Hy9/vrrslgsE/58L774oqxWq8xms+Li4nwe7/n3wePxeL+ura31HiclJclut09onQAAAMBk90Zljf7NcU5XWlrV1mmotbNLrV2dqm/rUHl9s888osGEBJm03Bqj5Glm3TkrTg8smS9reOgEVw8AwNRgDvP9nkjHHwAA/hPw4K++vl51dXVyu93Dvqe0tFT5+fl+n5HhdDr11FNPKT8/33uurq5uwoO/o0ePatu2bWNaIzs7W1lZWcO+vqioSAUFBSotLZXL5ZLb7ZbFYpHNZtPGjRuVkZEx4a8bAAAAGE/Pnb6gB371rtq7hhfu9WaLidT/S75Jn0mYrmXWWIUFB41zhQAAXB/MYb5vOXra6fgDAMBfAhL8lZWVac+ePSouLvbpUJusHA6HnnzySRUWFga6lAnncDi0efNmOZ1OSdc6BZOTk1VXVyeHw+H9k5OTo127dikzMzPAFQMAAABD2+M4pz8rcWg0kd9t0y169Jab9YeL5igkiLAPAIChWPp0/BH8AQDgL34P/rZv3+7tmDOGuY1OILjdbuXn5ysvL88bgl3v8vLyvJ2FGRkZys7O9unqc7vdevTRR70B6LZt2+R0OpWdnR2QegEAAIChGIahv3vrhP7hnZPDvichOkLL42K1Mj5Wv7dgtj4+d5rfdxsBAGAq69PxR/AHAIDf+DX42759u/Ly8rzHvX95ngxBoNvt1ubNm1VcXOxzPiMjQ4888ojWrVs3om1Jx0PPmX5ZWVne56+rq/Oe79052fMxSbLZbIM+R1FRkTf0y8rK6jfMs1gs2rdvnx588EFv+Jebm6tVq1YpPT192K8HAAAA8IfOLkNZxaXad7zvB/m+uiRBd86KU3hwkMKCghQREix7TKSWxcX0mUsEAABGxhzaO/hjxh8AAP7it+DP5XIpLy/PJ+zrDvq6Z8dZrVZ/lTMgi8XiDf0sFou2bNniM8/OarX6PfjrWdtEddc99NBDw36OJ554wmfb061btxL8AQAAYFIpvlitv3z9mN6qquvz2D+sXqbs2xbTxQcAwARhxh8AAIHjt+CvZ6efdC30695O0mw2+6uMYUlLS1Nqamq/8+sCWetEBaO5ubneMHPLli1DXm+xWJSVlaXc3FxJ17ok8/LymPcHAAAAvzAMQx/WNehUXaNmRIbpZku0pkeEyWQy6VRdg7a9cVzPn6vsc1+QSXo6NVkPrljg/6IBALiB9O6eZ6tPAAD8x2/BX3FxsUwmkwzDkMlk0oEDB5SSkuKvpx+Rffv2BboEv9qzZ4/367S0tGHds3HjRm/wJ0kFBQUEfwAAAJgQhmHoZF2jXrl4Vb+5eFWvXKjW5eZWn2tiQ0N0kzlKx2vr1d7Vd4RAeHCQ9n/mNv3+TXP9VTYAADcsZvwBABA4ft3qU7o21y8rK2vShn6T2UR0GxYUFHi7/SwWi+x2+7DuS0pK8jnuPRMRAAAAGC3DMHTG06jfXKi+FvZduKpLTa2D3lPf3qGj1Z5+H1tiidYPP3mL1syZNhHlAgCAXpjxBwBA4Pgt+OsOl0wmE51hozQRW30WFRV5v05OTh7RvUlJSXI4HD5rpaamjlttAAAAuPHknzyvvznyoc7VN415rWkRYfrO7Uu1eYVdocFB41AdAAAYDmb8AQAQOH4L/iwWizf8mz9/vr+e9rpQW1s7YWsXFBR4v7bZbCO6Nzk52Sf46+7qBAAAAEaqtbNTf15SpmeOO4d9z7SIMHna2vts7RkWFKT/l7xQO25bImt46AB3AwCAidJ7xp+brT4BAPAbvwV/ycnJbAc5RuO91afb7faGsdK1cHYkFixY4HPsdA7/TRoAAACg24WGZv3hy2/rjcuDf+BtTlS4Pjlvuj45d7o+MW+aFpmj1WVIFQ3NOu1p1Bl3oyJDgvWJudNki43yU/UAAKC33h1/zR2dau/sogMfAAA/8Fvw9/DDD3uDv4qKCrr+RqHnVp9Op1N5eXkqLi6Wy+WS2+2WxWKRzWbTxo0blZaWNuS8vt4desOd79etd4dgz+4/AAAAoLe2zi6db2xWR5ehLsOQIemsp0lf/817qmpu63P9rMhrQd8n5k7TJ+dN12JLtEwmk881wSZpgTlKC8xR+kzCDD+9EgAAMBhLWN+O+/r2DsUHhwWgGgAAbix+C/5SU1O1fv16HT58WIWFhXrooYfGtF5+fr527typY8eOjVOFU4PT6dS2bdv67Z50u91yOBxyOBzKyclRRkaGdu/ePeBaR48e9Tke6QzB8e5ABAAAwPWnpqVNh1yX9UL5Zb3kqlL9MGb8RIYEa9/Hk5WxOKFP0AcAACa/3h1/kuRp61B8BMEfAAATzW/BnyTt27dPa9eu1ZNPPqnMzEzFxMSMei2PxyOPxzOO1U1e3a+zoKBA+fn5w74vPz9fpaWleu655/rdxnOsf//i4uJ8juvq6oZ13/Hjx0f0PPPmzdO8efNGdA8AAAAC51Jji3569qL+9+wlFV+qUadhDH3T79xkjtL/fv4OrZo+sm3oAQDA5BEdEiyTpJ4/AXja2wNVDgAAk8qFCxd04cKFYV8/0kzFr8GfJD333HNat26dNm/ePKIQq7fy8vIbruOsex5fUlKSNm7cKJvNJrPZLI/Ho6KiIhUUFPjM7JOubb+5efNmHTx4sM96tbWDz1AZSu+//8MNErOzs0f0PN/61rf0l3/5lyO6BwAAAP5hGIZaO7tU3dKmF8or9dyZiyq6WK3hR30fWWebqfzP3Ka4cLoBAACYykwmk8xhIXK3fdTp72kbuusfAIAbwcGDB/VP//RPE7a+34K/+vp6SVJQUJC38y8tLU27d+/uMytuKEePHlVBQcFElDkp9eyk27VrlzIzM/tck56ert27dysnJ0e5ubk+jxUXFysvL6/Pfb1DwhstSAUAAMDIdHYZ+vEJl54qK9cpd4NaO7vU3jWaiM+XLSZSWYkL9OgtNyuIrT0BALgumMNCCf4AAAgAvwV/d911V5+OsKNHj2rt2rWjWs8wjBtq3ofFYtFzzz2npKSkQa/Lzs5WXFyccnJyfM7v3Lmz38Cwp7Fu/UlwCAAAcP16uaJKf/XaMTlq6kd8b0RwkD6bMEO/t3C21ttmaXpEmEwmySTT7/6qG+pnewAAbgTmUN+3HQn+AADwD78Ff2lpadq/f7/32GQyyTAMGSOY93GjyszMHDK06ykrK0t5eXlyOp3ec263W06nU3a7fdzqslqtgx4PJCcnRytWrBj28zDfDwAAIHAc1R49+vox/bziyojuiw0N0e8tnK0/vGmOPpswQ1Ghfp8yAAAAAsgc1iv4Y8YfAACSpPvuu08pKSnDvv748eMjGqHmt9++N2zYoP379/t8kncsn+olMBzc448/rk2bNvmcKy4uHtfgr+cWpCOxYsUKrV69etzqAAAAwPi71Niiv33rQ/3oQ5eGu5tnVEiwNi6YrS8vmqt1tpmKCAme2CIBAMCk1Sf4o+MPAABJ15qdJrLhyW/BX0pKiiwWizwejze0s1gskobfKdYdNPWeTYe+UlNTZbfbfbr+en4tffT3v9tog7xubPUJAAAwNRiGoWdPntezJys0PSJcKXPi9fG507QiLlZNHZ36/vtn9MT7p9XY0dnv/Unxsfr7O5bJHhup8OAghQUFKTw4SLOjIhQWHOTnVwMAACYjc1iozzHBHwAA/uHX/XbWrFmjQ4cOKTMzU48//vio13G73Vq/fr1cLtc4Vnf9SUxM7BP29RQXF+dzPNIZf72vv+WWW0Z0PwAAAPyvqb1DDxWV6tmT573nDp6+IEmaHhGmIJNU1dzW771zosL1vdXL9UdL5ys4iJl8AABgYL1n/Lnb2OoTAAB/8OvHcbuDofT09DGtY7FYtGbNmnGo6PrWe1vP3se9O/TKy8tHtH5tba3Psc1mG9H9AAAA8K9znkbd+38lPqFfT1db2voN/aJCgvWd25fq5P2f1teX2wj9AADAkNjqEwCAwPBrx19iYqIMwxj21p6DWbBgwZjXuN717ujrHcytWrXK53ikW3327vgbz/mBAAAAGF8/d1Vp0y/fUW3r8D9tb5L09WU2/cPqZZoTHTFxxQEAgOuOha0+AQAICL8Gf6tWrVJaWlqf2XKjkZiYSNffEHp35KWmpvoc9w4Cy8rKRrR+761Wk5KSRnQ/AAAAJt6lxhY9/t4pPek4J6PXY7GhIVpvn6nXKmtV0dDs89jn5s/QE3evUPK0sf/sDgAAbjx9Ov7a2eoTAAB/8GvwZzabtXfv3nFZKyUlRSkpKeOy1vXK7XZ7v05LS+vzuMVikcVi8V432DzA/vTcGpR/FgAAAJNLZVOLdr93Wk8fK1dLZ1efx5dZY/T82ju0LC5WklTuadJvL17VGU+TPjlvuj45b7q/SwYAANeR3h1/NS0EfwAA+INfgz8Mn9Pp1KZNm3T48OFRd0iWlJR4v/7mN7/Z7zVr1qxRYWGh99jhcAy7c6+0tNT7de9uQgAAAPiXYRgqr2/S21fceuXCVf3HiQo1d3T2e+3vL5yjH3/qVsX2+CT+AnOUFpiZ2QwAAMbHzMgwn+MrLX3nCAMAgPEXsOCvvr5ee/bsUXFxsZxOp3de3K5du3T//fcPeF9ZWZksFovmz5/vr1InhNvtVnFxscxmc7+hmdVqldPpVE5Ojnbv3j3i9Z1Op7eDLyMjY8Awb+PGjT7BX3Fx8bCDP4fD4f06IyNjxDUCAABg9LoMQ+9eceuw67JKKmv0dlWdaoaY3xcaZNJ371imbbfeLJPJ5KdKAQDAjWhmZLjP8eWmVhmGwc8gAABMsIAEf4899phyc3MlXftkcjeTyTTkdpMvvPCCnn76ae3evVubNm2a0Dr70x1QjoXT6dS6deu8W2ympKTo4MGDPtdYLBalpaUpPz9fjzzyiOx2+4ieY9u2bZIku92u7OzsAa9LT0/3Oc7Ly1NWVtaQ6xcUFHi/Hq+5jQAAABhcbWubXq64okPOy3qpokpVzcP75HxIkEl/vNSmHbct1gJz1ARXCQAA0Df4a+vqkqetQ5bw0AHuAAAA4yHI30/40EMPKTc3V4ZheD/l0/1HGnrO3I4dOxQbG6utW7eqoqLCHyX7qKur8zkeTRCYk5PjM3+vuLhYeXl5fa7LzMyU9FGIN1y5ubkqLi6WxWIZ1lahPYNBp9OpoqKiIZ9jz549/d4PAACA8XOtq69OOe+c1JrnSzT9P17Sfb94Rz85eX5YoV9IkEl/stymk5s+pX2fWEXoBwAA/Kb3Vp+SVNXcGoBKAAC4sfg1+Hv66ae9nWLdQV93ANj9x+VyDbnOjh07ZBiGt2vQX4qKinwCO0l69tlnR7xOf6+xv8AzNTVVFotFxcXFysnJGdbaOTk5ysnJkd1uH/Z8wKysLJ+Owu3bt/d5nT3l5eV5t/nctWvXiLsRAQAA4MswDL1dVacfHHfq229+oMxfvqOU50s0+8c/18d+WqRvH/lQr1bWqMsYeq1pEWH6/PwZ+rvbl+jEpk/p3z9xixaaoyf+RQAAAPQQFRqimNBgn3MEfwAATDy/bfXp8XiUk5Mjk8nk3d4zLS1NqampstlsiouL06OPPjpkx590rRPuscceU15enh577LEJqdfhcKi4uFi1tbVyu91yuVwqLi7uc11+fr5KSkqUmJgou92uuLg4JSYm9ju3r9vGjRt95uN1n+tPenq68vPzlZubq/z8fGVkZCglJcUbtnk8Hm+XXkFBgdxutzIyMkY8F/Dw4cO6++675Xa7vVuR7t27t8+8v9zcXG8ImZWV5e1KBAAAwOgcq/FoS7FDr1ysHtX9SyzRWmebpTVz4nX7DKvssZHMzgEAAJPCzMhwNbQ3eY8vE/wBADDh/Bb85efne79OS0tTdna2bDabzzU2m21YHX+StGbNGh06dEiHDx/WunXrxrVWSSPqsnM6nT6BZXegOZCsrCzV1tYqPz9fVqtVO3bs6BOwdXvggQe8f+/cbrdyc3MH7HRMSUlRdnb2gGsNxmKx6PXXX9ejjz6qwsJCOZ1OrV27Vna7XYmJifJ4PCotLfV2Au7du7fPfEAAAAAMX31bh/7+7RP6V8dZdQynle93IoKD9Kl507XONkvrbDO1yEI3HwAAmJxmRobrrOej4G+484kBAMDo+S34KyoqkslkUkZGhh5//PEBrxvuzDy73S7DMPSzn/1sQoK/rKwsZWVljfu63bKzs4c1Gy8pKUnHjx9Xfn6+ioqK5HK5VFdXJ7fbLbvdLpvNptTUVKWlpY15y02LxaJ9+/bJ4XDo2WefVUlJierq6lRYWCi73a7k5GSlp6fT5QcAADAGDe0d+t+zl/TXb36gi40tw7pnkTlK6+2ztN42Sx+fO02RIcFD3wQAABBgMyPDfY7Z6hMAgInnt+CvtLRUkgYN/axW64jXHW6H4FRmsVgmPIjsKSkpacRbhQIAAEC63NSi1yprJV2btRcfHqr4iDA1d3TqsKtKBc5K/eZCtdq6uvq9f05UuFbPjNOC2CgtiI3UAnOUEuNjdbMlxp8vAwAAYFzMjAzzOSb4AwBg4vkt+HO73YNufzlS3VtrDmcmIAAAADBR6ts69Py5S8o/dV6/PH9FI9i10yssKEiP3rJIO25brKhQv/2IDgAAMKFm9er4u9xE8AcAwETz27sK3dtSjpfuTr/hbg0KAAAAjJe2zi695KpS/qnzeqG8Ui2d/XfwDcfn58/Qk2uStNhKVx8AALi+9N3qkxl/AABMNL8Ffzabbdy25XS5XHI4HDKZTOMaJgIAAAAD6TIMlVyq0f5T5/XfZy6qprV91GuFBJmUOmeavpm0UL+3YLZMJtM4VgoAADA5MOMPAAD/81vwl5iYqGeeeUb19fWKjY3t95q6urphrZWTk+P92mKxjEd5AAAAwIBeclXpkeJSnfU0DXltVEiwZkeFq6alXXVtH4WD0yPCtN42U+kLZutzCTNkCQ+dyJIBAAACjhl/AAD4n9+Cv29+85t6+umntXPnTj322GOjXqewsFCFhYXeT0WnpKSMV4kAAACAj46uLn3nrRPKeffUoNeFBJn0+fkzlbF4njYumK3o383p6+jqUl1ru1o6uzQnKkLBQXT2AQCAG8esKN+Ov5rWdrV3dik0OChAFQEAcP3zW/BnNpu1fv165eXlKTk5WZs2bepzzVDz+rqDQ5PJJMMwZDKZlJmZOVElAwAA4AZW2dSiTb94R69crB7wmntnxytj8Tx9adFcTe+1lZUkhQQF9XseAADgRtB7q09JutLSprnREQGoBgCAG4Pfgj9J+va3v61Dhw5p69atKigo0I4dO7Ry5Urv4wNt9bl//37l5eXJ4XDIMAxJkslkUkpKiubPn++P0gEAAHAdKqv26CcnK3Skqk7hwUGaExWh2VHhigsP1b+UnlVlU9/tqFbGxSpjSYI23TxPC8xRAagaAABgaogPD1OQSeoyPjpX1dxK8AcAwATya/Bns9m0Y8cO7dy5U0VFRSoqKpLZbNaqVatkNpvlcDhkMpm0fft21dXVyeVyyeFwSJJP4Nf99a5du/xZPgAAAK4DV5tbdeD0Bf3niQq9c8U97PtCgkx64q4V+n/JN3m3nQcAAMDAgoNMmh4RpqrmNu855vwBADCx/Br8SVJWVpaKiopUUlIiSXK73SouLvY+bhiG8vPzvV/31PMNlscff5xuPwAAAAyoqb1D/3Xmot676tb5hhadb2zW+cYWXWpskTH07T4SoiP0X5+7XXfPjp+QWgEAAK5XMyPDCf4AAPAjvwd/knTw4EE9+OCDOnTokE+Y1/11z+6+nrrP7927V2lpaX6qFgAAAFPNkcu1yvjVuzrtbhzzWmvnz9Szn76VWX0AAACjMCsyXGWq9x5f7mcrdQAAMH4CEvxJ0r59+5SXl6enn35aTqez32t6d/ylpaUpOztbNpvNHyUCAABgiunsMvT4e6f0d2+dUKcxvL6+udERylg8T9MiwlTZ1KpLTS261Niq1s4u3XfzXP1Z8k0KYmtPAACAUZnZ68NTPbv/AADA+AtY8CdJmZmZyszMVFlZmYqLi1VeXu6d7We1WmU2m2W327Vq1So6/AAAACBJOt/QrG+WOFR8qUYJ0RFKnmZW8jSzllhi9MT7p1VSWTPkGhHBQfriwjn6o6Xz9ZmEGQoOItgDAACYCH2DPzr+cP0wDENXmtvkbGjS3KgIzYuJDHRJABDY4K9bYmKiEhMTA10GAAAAJrmC8kp97Tfvq7rl2ifFq1vadLTaM+g9t8+w6vPzZyghJlIJ0RFKiInUEku0okInxY/CAAAA17WZkWE+xwR/mGo8be0652nSufom37/+7uumjk7vtX9962LtvGt5AKsFgEkS/AEAAACDae3s1PY3PtC/lJ4d9j1BJulvPrZE3/7YEoUEBU1gdQAAABjIrCjfjr/LBH+YIs64G7Wl2KGXKqqGfc9j753S/YvnKXGaeQIrA4DBTdl3QAoLC3X//fcHugwAAABMsNPuBt37fMmIQr+FsVEq/sIafeeOZYR+AAAAAcSMP0xFz56o0C3//cqIQr9uP/zQNQEVAcDwTdmOP5fLpeLi4kCXAQAAgFGqbGrR/lMX5G5t1+/fNEerplt8Hm/p6NTu90/rsXdPqaWzq8/9v79wjj45b7ocNR6VVnvkqPaotatLX1s6X/94z0qZw0L99VIAAAAwgP5m/BmGIZOJGcsIDMMwVHSpWi+WX1ZTR6dun2HVPbPjtNQaI09bh7KKS7X/1IVRr/+TExV6/K7lCg8OHseqAWD4pmzwV15eHugSAAAAMArvX3XrX0rP6sCpC2rruhboffedk7pndpyyVi7UHy6ao5crrujPXy3TWU9Tn/vDg4P0z/es1EMrF/i8YdRlGDIMKTiIN5EAAAAmi94z/lo7u1Tf3sGHtDBhzrgb9f5VtxJiIrU8Lsb771pLR6cOnr6gf3Wc1ftX+84Jjw8PVVhwkCqb+m5Ha5I0NzpCC2OjtNAcpYWxUVrwu78Gm0xK/dmr3mtrWtv1f+cq9ZWb503YawSAwUzZ4K+kpERmM3slAwAATAWGYeiQq0rff/+0XrlY3e81r1XW6rXKWj1UFKyG9s5+r1lqjdFzn/1Yn+5ASQoyma79Rg4AAIBJo3fHnyRdbmol+MO46+jqUs47p/QP75xUp2F4z8+NjtAya4zKajyDbjVb09re7/mvL7Ppn+8dfEeRT8yd5vN7zg8+cBL8AQiYUQV/Ho9H69atG+9ahs3lcskwDFksfd/wAQAAwOTyQW29/qzEoV+evzqs6/sL/YJM0iOJC/XYncsVHTplP7sGAABww4kODVF0SLAaOz76Ga+quVWLrTEBrArXm3JPkzJ/9a5erazp89jFxhZdbGwZ8ZrWsFDt+8QqfWnR3CGv/ZPldp/g75fnr+qcp1ELzdEjfl4AGKtRvWtiNpvldDplMplk9Pj0hL95PH1bsgEAADA5eNra9Q9vn9S/OM6qo6v/nxnDg4NkCQsZ9JO3a2bHa09KUr9dfgAAAJj8ZkaG61z9R1u4D/azHzBSB06d10NFpfK0dYzovkXmKK2aZtEbVbV9gsGUOfHK+/RtssVGDWut379pjqzFoapr+6hr8D8+rNB3Vy8bUU0AMB5G/XHptLQ0FRYWBmwQbyADRwAAAAzsanOr/uvMRX3vnZO61M98DEmaFRmuRxIXaPOKBYoLD9X/lVcqt+ycz6dkZ0eF64m7VyhjcULAfuYEAADA2PUN/vr/GREYTG1rm/7vXKVO1jXoYmOLLjS2qKKhWSfdjX2u7R773d/nDz81b7r+PPkmrbfNUnDQtcaWioZmvVZZq2O19Vpiidb9ixNGNDs8MiRYmUsStKfsnPfcjz506e9uX8oMcgB+N+rgb+PGjSosLJRECAcAAHAj8bS1q7qlTRHBwQoPDlJEcJDaugy9UF6pg6cv6BfnrwzY4ZcQHaF/WL1MmxbPU3hwsPf8lxbN1ZcWzdXxmnr937lLig0L0VeXzJclnNkvAAAAU93MyDCf48sEf+jhclOLnnSc07n6Jn0uYYa+cvM8RYR89LtCe2eXnj5Wru+8fUK1A8zh62lhbJT2f+Y23TrDotPuRn1Q26AP6xoUHhSktbaZSppm9rneZDLJFhs17O6+gfzJcptP8HehsUU/r6jSevusMa0LACM16uAvJSXF+/XevXuVlJQkq9U6HjUNqK6uTpJUWlqqbdu2sdUnAACAn9S1tut/z17S/lPn9esLVzXSj32FBQXpr25ZpB23LR50Rt+K+FitiI8dW7EAAACYVGZFhfsc0/EHSapv69D33z+tfzx6xjsDcv+pC/rL14/rT5fb9NDKBfqgtkHfeq1MH9Q2DGvNB5YkaE9Kksxh1z5AuDLerJXx5iHuGh+rplt0+wyr3r5S5z33gw+cBH8A/G7UwZ/ZbJbFYpHValVaWtp41jToc0qSzWZTUVGRDhw44Jfnxfj6xje+obCwsD7nH3zwQW3evDkAFQEAgLbOLj3x/mm9cvGqIoKDFRceKmt4qKxhoTpWU69C12W1dnaNau11tpn613sTtdgaM85VAwAAYCqYGdk7+GPG342srbNL//6BU999+0S//y5Ut7Tp8fdOa9d7p4f9gcP48FDtSUnSpsUJ41vsCP3JcptP8Pei87Iqm1o0OyoicEUBmDL27t2rffv29Tnf1jay75ujDv4kKTk5WW63eyxLjNpEdxdi4tTU1PR7vqFheJ/cAQAA4+tqc6v+8OW39dse8/XGKjIkWBvss/T1ZTZ9bv4MZvQBAADcwPoGf3T83WgMw9Abl2v13OmL+q8zFwacBe5zzwDnPzd/hpZbYzUvOuLan5gI3Tkzzmd70EDZtHievvXaMTX9roOxo8vQT05UaOutiwNcGYCpoKGhQZWVlWNeZ0zBX1JSkhwOx5iLGA2bzcZswSkqPj6+346/mBi6AAAA8Leyao82Hj6ic/VNY14rNMiktfNn6r6b52nDgtmKDRvTj5oAAAC4TvSZ8TeM0AdTX0dXl45U1XlngTvrmwe8Nsgk3TLNonevDtxk8rEZFv3LvYlaM2faRJQ7Lsxhofryorn68YkK77m8k+cJ/gAMS0xMjGbPnt3nfFtb24ANVf0xGaRnmGBHjhzRF7/4Re/x888/r9WrVwewIgAAIEkvnKtUxq/eUUN757CuDwsK0nr7TN2/OEGfSZguw5BaOrvU0tmpts4uJcREKmaQ+X0AAAC4Mf36/BV9+sXXvcfx4aGq/vq6AFaEiXKhoVk/r7iilyqq9IuKK6prax/yng32Wdp553IlTjPrw9p65R4r148/rFB9e4ckaXZUuHbeuVx/tHS+gqbATiKvXLiqT77wmvc42GRS05+mKSw4KIBVAZjKRpqx8M4MAADAdcwwDH1Q26Bfnr+iC40tqm1tV01rm6pb2vTbi9V9ts+ZFx2hB1fYVd/Wobq2dtW2tis0KEifSZiu379pjuLC+3btAwAAAIPpvdVnTWu72ju7FEoQcl3wtLXrv89c1E9OnFfRpeGPD7hndpweu3OFUud+1MG3LC5W/7YmSTmrl6vQeVkdRpd+b8GcKbWbyK3TLT7HnYahs55GLYuLDVBFAG40U+f/mAAAABiWts4uFV2q1ovllSpwXtZZz/C28bxzZpyeX3uH5kQzeB4AAADjp3fwJ0lXW9r4uXMK6+jq0i/PX9VPTlTo+XOX1NLZNaz7llpjdN/Nc/WVm+dp+SBBWGxYiO5bPG+8yvUrS3ioZkWG63KPWZYn6hoI/gD4DcEfAADAdSTvZIX+4tVjutrSNqL7Mpck6N8/vkoRIcETVBkAAABuVNMiwmSSfHabuNzcSvA3BTmqPfrPExXKP3VelcOc1Zg8zaz1tmuzwJOnmWWaAtt1jtVSa0yf4A8A/IXgDwAA4DrQ2tmpPy8p0zPHnSO6L9hkUs6dy7T1lptviF/AAQAA4H/BQSZNjwjTlR4fTqtqHl5oBP/pMgw1dXSqvq1D7rZ2VTa16lJTiy42tuhSU6t+deGK3r/qGXIda1ioPjd/htbaZupzCTM0LybSD9VPLkutMT7bnp6oawxgNQBuNAR/AAAAU5yrvklfevltHamqG/CahOgIrZkzTdMjwhQfHqq48FBNiwjTZxJm8ElrAAAATLhZUeEEf5PE1eZWvXPFrbev1OntK3V6/6pHV1ta1dje2WcG+HCFBJmUbp+lry6Zr/X2mQoPvrF3EllijfY5PknHHwA/IvgDAACYQroMQ+62dl1tbtPVljadcjfqW68dU3U/W3vePsOqjQtmacOC2Vp1g2ypAwAAgMnp2py/eu9xVfPItqbH6BmGofeuuvVfZy7qf89e0in3+HWf3THTqq8uma/7bp6r6f3McrxRLbXG+Byz1WfgXW5qUXVLu5ZaYxQcxO/GuL4R/AEAAExiVU2tKqmsVsmlGhVfqtHRarfauwb/HG5kSLD2pibrgaXz/VQlAAAAMLiZvUIhOv4mRpdhqLa1XZVNLapsatVvLlzVc2cu6vQ4hn3zoiP0wJIEPbBkvlbEx47buteT3sHflZY21ba2KS48LEAV3bi6DEPfffuEdr57Su1dhhbERunPkhbq68tssoSH+lzb2WXog9p6Hamq0/K4GN09Oz5AVQNjQ/AHAAAwyXQZhvYdd+pfS8/qwxF+MvRmS7T+9/N3KGmaeYKqAwAAAEZuZqRv4HG5afIHfxcamvXa5Vp1dhkKMklBJpOCTSZFBAdpoTlKN5mjFRYcFOgy1dTeoe+9c0r5p87rYlOLOob4oOBwWcJCNCcqQnOjIzQnKlwJMZH6TMIMfXLudDqmhrAwNkohQSaffxYn6xp15yyCP3+qbW1T5i/f1SFXlfdceX2TvvXaMf3tWx/q68tsumd2vN65UqcjVXV6u6pOjR2dkqRHEhcQ/GHKIvgDAADwsy7DUGeXodB+3iQ462nU13/zvn57sbqfOwf3hYWz9eNP3trnU4sAAABAoM2aYh1/Pz1zUV/99Xtq/l0I0J8gk2SPidJia7RumWbRnyff5Pf52TUtbUo/9KZev1w7ovsigoN063SLbp9p1e0zrFpkjlJsWIhiQ3/3Jyzkhp/TNxahwUG6KTZKJ3t0Wp6oa9Cds+ICWNWNpbTard9/6S2d8TT1+3hDe6f+zXFO/+Y41+/jR6rqJrA6YGIR/AEAAPhJc0ennjlWru8fPaNLjS1aPTNOGxbMUrp9lpKmmbX3WLkeff249xOGQ4kKCdb0iDDNj4nU15bO1zeW25jjBwAAgEmp71afk3PGn2EY2v3+aW1/44Mhr+0ypHP1TTpX36SXK67ov89c1Jt/kKIZfpp1d76hWZ8veEPHa+uHvljXutC+fPNc/cFNc3TrdItCggLfrXg9W2qN6RP8YXy1d3bpmePlKnReVktnl+LCQxUXHqrI4GD9+ESFmob5u3V/3r/qVmtnJwE4piSCPwAAgAnW1tmlH37g1PfePaWLjS3e829W1erNqlp9+8iHMoeFyNPW0e/9MaHBuntWvFLmxOve2fFabI3RtPBQRYXyoxwAAACmhskw488wDJ12N+o3F6/q7Sq3IkKC9Jl5M/SZhOmKCg1Re2eXHi4q1Q8/dI1q/XP1TfriS2/pVxvvnvCw4ERtgz5X8LpcDc0DXhMfHqqEmEh9LmGGvnzzXN0+w8oHBf1oqTVGLzove48J/sbXG5U1evC3R+WoGV7wPSMiTI/dtVyHXVV6/twlDbYjrj02UqtnxqmutV2zogj+MPXwbhEAAMA4+LC2XvmnLqjoYrU6jC5Fh4QoKiRY0aHBerWyRs76gX8hl9Rv6GeLidTTqcn63PwZfBoXAAAAU1qfGX/NrTIMQzWt7fr7t0/IWd+s1Dnx+vpym+LCx28OmmEY+ll5pX565qJeuVitCz0+iCdJTzrOKSI4SJ+aN10N7Z0qutR3y/0llmiFBAWpyzDUZRiqbW3XlZb+OxZfrazRn75yVP/5qVt9QrbjNfV6tbJGd8+KU+IA87gNw1DRpWpVNrXqYzMsWmSO7hPUtXZ26pfnr+prv35PV3vVMC86Qv/5qVu11BqjmZHhk2L+4I1siTXG5/gkwd+48LS1a8ebHyi3rFzDnWa5eqZV//P5O5QQE6lvLLer3NOkPWXn9H/nLqmhvVO3TDdr9cw4rZ5p1R0zrZoV5d8te4HxNmWDv/r6ejmdTiUmJga6FAAAcIO63NSig6cvKu/keb19pW5c135whV1P3L1C5jDm9QEAAGDqmxXl2/HX2tklZ32zNh5+09ux80J5pf72rRPKXJKgLYkLlTRAQDZcXYahP/71e/rJyfODXtfS2aVDrqo+502Sdt+9Qn+5alGfAM7d2q5T7kadcjdo2xsfqKJH592zJ89reVys/vq2xTpV16C/eetDPXf6ovfxXXct19ZbF/us197ZpW+88r6e7VGrPTZSn543Q5+eN13utnYddlXp1xeu9jsaYKk1Rj9Pv0v22KhBXyv8Z2mv4O+Uu1FdhqEgui77eO+KW3/+apk+rKvXMmus7pxl1Z0z43TnrDhZwkJVVuORo8aj0mqPnj9X6bOTzlA2r7DrX9ck+nThLjBH6fv3rNT371k5ES8HCLgpG/zl5eVp586dqqioCHQpAADgBtLY3qGfnatU3qnzerniijqN4X7G8COfTZihP142X29crtWL5Zd1rv6jYePzYyL1g0+s0ufmzxzPsgEAAICA6r3VpyR9+sXXdNbT5HOuqaNT+447te+4U5+YO01bEhfq9xbO7rMDhmEYKq326N2rbq2eadXK+L4h4XfeOjFk6DeQyJBg5X36Vv3+TXP7fdwSHqrbZ1p1+0yrVsTF6t7nS3wCuR1vfqAjVdd+3u/9O8O2Nz5QQ3un/v6OpTKZTGrp6NSXX37bZ1tISXLWN+tHH7r0oyG2Hr1jplWH1t+p6X6aLYjhWWqN9jlu6eySq75ZC8yEs906urq0673T+s7bJ9Txu703q5qr++28HcyqaWZ9bel8edo7VNfartrWdnUYhr6wYLb+YFH//w0D17MpG/zV1tYGugQAAHCD6DIM/frCVT17okL/+7utQEbj3tnxyrlzmT4+d7okadPiBP3LvYn6oLZBv75wVaFBJm1aPI8uPwAAAFx3okOvbYXf1CMc6x369fbKxWq9crFa82Mi9fDKBfrT5TY1dnRq/6kLyj95XsdqP5rt9de3LtZ3Vy/1BoR5Jyv0D++c7Hfd2VHh+vjcabrc1KriSzV9grlZkeF6cf1q3TEzblivbdV0i/Z/5mP6wktHfLYe/L9zlQPe8w/vnFRDe4e+c8dSfeHwW/rNxavDeq7ePpswQ/+79g7FMP970pkZGd5nlvtJdwPB3++cdjfoq796T69fHv37/JEhwfrO7Uv0F8mLFMrWtoDXlP2O4HQ6A10CAAC4zlW3tOnHH7r09LFynRniTQnp2hsEmxbP0zJrjBo7OtXY3qHGjk4Fm0z61Lzp+tS86X22CDKZTFoRH6sV8bET9TIAAACASWFmZLjK6/v/uTouPFT17R3erp+eKhqatePND/R3b32o9n4el6TH3jul4kvVOvjZj+lcfZO+8ZujPo8Hm0zadddypdlnaak1xvtzeW1rm37uuqIXnZV694pby+Ni9M/3Jo54y8yNC2fribtX6K9ePz7se/659KyePXm+z6y+oZgk3T7TqvtvnqesxIXM8pukTCaTllpj9FZVnffcibqGG3Z3F8MwdL6xRe9dcevNqlr9a+nZfretHa7PzZ+hp1OTdZM5euiLgRvMqIO/kpKS8axj2Dwej44eParCwsI+b5wBAACMh3ev1OlJxzkdPH1BLZ1dg14bFRKs379pjjIXJ+jTCdP7bEEEAAAA4JqZkWH9Bn+zIsNV9IV7FRMaomeOlWvv8XJVNfcNwwYK/bqVVNbolv/+rQxJbV2+P8f/25pEZSUu7HNPXHiY7ls8T/ctnjeyF9OPb61apA/rGvSDD3y35owIDtKfJd2kudER+otXy3y6AnuHfuawEBWuv1PWsFD96sJV/er8Fb1ZVauwoCB9Yt50rZ0/U5+bP0Mz2NZzSugv+LsRdHYZOuVu0HtX3T3+eFQ9SMgdZJK+lbxIESHBevNyrY5U1cr9u27J4N+FqEnTYpUcb1bKnGlaMyeefAAYwKiCP4/Ho/vuu4//sAAAwHWlvq1D2944rqePlQ96XZDp2pY6mUsS9IWFc9hWBwAAABiGWf2EVXHhofrFhru1xBojSfru6mXK/thi/fTMJT3pOKc3q0a2DWB/3XN/lrSw39BvvJlMJj2VkqzqljY9f65SwSaT/mS5TX/zsSWaFxMpSZoREaav/vq9fmeFz4gI08/T79atMyySpMRpZv2/5JsmvG5MnCUW32606zn4MwxDPyuv1L+WntWRqjqfbX2HcrMlWj/51K26e3a891yXYeiMu1GtnV1abI1WeHDwRJQNXJdG9S6V2WyW3W4P2HabBI4AAGA0jtfUK//UeVnDQvW5+TOUPM3s/bni5Yoq/ekrR+VqaB7w/qT4WP3xMps2LZ6n2VER/iobAAAAuC70nm0WGxqin6ffpaRpZp/z4cHByliSoIwlCXqrqlZPOs7pudMXvV18ceGh+vKiucpYnKDQIJPu++U7ctb3/3N8mn2W/umexIl5Qf0ICw7S/3z+Dp3xNGp6RLis4b7zu+9fkqCo0GB95eV3fLoS58dE6hfpd2tpXIzfasXEW2r1/ed5sq4xQJVMrHeu1Olbrx5T0aXqEd/70Aq7vn/PSkX3+kBtkMmkxVb+ewBGY9QfT09LS1Nubq73zTKjn0+p9Kd3aDec+3reYxjGsJ8LAABAuvZJwX86ekY73vzAuz3Q1jekudERWjt/pjq6uvSTk+f7vTc0yKQvLZqrrJULdM9sthIBAAAARusby2z6zxMV8rR1yBoWqp+tW607ZsYNes8dM+P0k0/H6Ym7V+jliiuaHhGmTyVM9+n+ee9LH9cf//p9/ay80ufe5GlmHfjMxxQc5N+f4U0mk262DBxYfGHhHL24frUyfvmurra06ZbpZv1s7WrZRjhXEJNf7+DP1dCspvYORU2iXWPerqrTc6cvqKq51ed8l6S61nZVt7SpuqVNV1va1NLZpZvMUUqKNytpmlkr4mL0v2cvDfj7dH8sYSG6ZbpFt0636MuL5vp0+QEYH6P+P8zGjRuVm5srSbLZbEpMHPqTM2VlZXI6nTKZTDIMQ0lJSbLZbIPe4/F4VFpaKrfbLZPJpNTUVCUmJsrtdquurm605QMAgBvE+YZm/dGv39OvL1zt89jFxhb96ENXP3ddm923/dab9eAKu2bR3QcAAACM2arpFh37yif13lW37p4Vp+kjmFM3KypCDyyd3+9jceFhen7tHfqX0rPa/sYHauvq0s2WaL24brViwyZPwNLT5+bP1NmMz+h8Y7OWWGL8Hk7CP27utdWnJJ1yN2rVdEsAqvFVWu3W3x450ScwH0pZTb3Kaup14PSFIa+dExWuW38X8t063aJbZ1i0MDaKD9QCE2zU3/m6gz6z2axXX311WPfce++93q8zMzP1+OOPD/v5CgoKtHXrVhUXF2vXrl2aP7//b/QAAACS1NjeoQLnZT1cVKra1vYR3fvJudP1g0+u0k3mvr+kAQAAABi9hJhIJfxu3t14MplM+otVi5SxOEEf1NXr9hnWPlsHTjaxYSFaHhYb6DIwgaJDQzQ/JlIVPUZKnPRj8Pd6ZY1Kqz2KCglWbFiIYkNDFGwy6Znj5Xru9MUJeU5bTKT+YfUyfX7+DD5ECwTImL772e12WSzD+59Uz24/m802otBPktLT05WSkqJ169Zp06ZNeumllxQTwx6/AABAqm/r0N7j5Sq5VCNXQ7NcDc2qbmnr91qTrr3ZUNHPLL+Y0GA9cfdKPbjCriA+gQgAAABMOTOjwjUzavidhMBEW2qN9vn98/+zd+9xcd33nf/fAwJdmQO6WbbEjGwndiIxknOxEmOGJG2ykQRi182vsaQhbXezFRK2N22yAlmTbbfbolva7CaWiaHNbnfNCHm7bVp7kJSmaRMGy4mdi2EQju+aQbJ1s5gzSJaELuf3h8qEAYQYGGYG9Ho+Hjw858w53/NBTjw2bz6f76uRcxP+zL6r1/SHz3eq/sjRCX9Wv7ycadr+0Q/qKyvu0sxp2Te/AcCEGVfwV1RUpO7u7lFd++yzz8Zee73eMT3PMAw1Nzdr7dq1evjhh9XS0jKmdQAAwNThP3pC1YHgsEHeYIVzZur//MZH9Kk75um1yHkdDJ/Uwe5T6jrbq+JFc7XngWVysq8GAAAAACBJ7jHm6J+O/XrriYkO/t49f1G//Y8/0/Mnzo76nnkzcrXxg4s1Izsr7rw9N0fzZ+Rq3oxczZueo+wsm7rO9ip4tlfB96IKno2q75ql371nif7k/g8RugMZYlzB32OPPaZQKDSqawOBgKTr+wGuXbt2zM90Op16/PHHtW3bNm3ZskXf+c53xrzWWJmmqb1798rn8+mFF14YdddjKoRCIbW0tKipqUnbt29XeXn5Te8xTVPPPfec8vPzZbfbVVAQv6my3W6PvY5Go7HXPT09sWOXyyWn05mk7wIAgJs7+f5FfeX5zlGPJ/ni3XfoqU+tUMH0XEnSvQVzdG/BHP3ByrsnskwAAAAAwC3s3vz4qXXjDf6uXrP03zve1P95tVvzZuTqM4vn67NLFuj+Bfn62emIvvD9l/Tu+5dGtZaRO01fW3m3vrLiLtlzc0Z1z6fumD+e8gGkwLg7/vr3+ruZcDgsm802qiDqZiorK7Vt2zb5/X6tXLlSmzdvHveaoxEKhfTkk0/K5/PFzkUikYwJ/jZt2jSmLsj29nbV1taO69ler1fV1dXjWgMAgOFcvWbpzeh5vfv+RZ18/5JOXrik4+cvqrErNKq9++4xZsv7sXv0pXuWsIE4AAAAACClBgd/r0XOybKsMf336bFzF1T5w1/ox++8Fzv3o3fe0x+/9Krycqbp4tWrunzNirtnRnaW7s2fo97LVxTtu6Ley1e0aNZ0eT64RP/5vrtjvxwLYOpI2Q63pmnKZrMlrSvM5XIpGAzq6aefnvDgLxgM6oknnsjo0aJ+v39IfQM79QAAmCx6+67op6d69Py7Z/X8ibP6ycke9V6+Mqp7f3PxfK3/wGI58mbKMWemCufM1OyclP3rDgAAAAAAcQYHf2bfFZ26cEm3zZqR0Dp///a7+vK/vKyzN/gF2OH+u9mZN1Pf+/wqfWRBZjSuAEiNlP0kzOl0Khw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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "filename = \"bond\"\n", - "for mode, mygray in zip(['light', 'dark'], [colors[\"mylightgray\"], colors[\"mydarkgray\"]]): \n", - " fig = plt.figure(figsize=(18,9))\n", - " ax, n, l_tot, c_tot = [], 0, 2, 1\n", - " n += 1\n", - " ax.append(plt.subplot(l_tot, c_tot, n))\n", - " ax[-1].plot(time, length, color=colors[\"myblue\"], linewidth=3)\n", - " complete_panel(ax[-1], None, r'bond length (\\AA)', legend=False, axis_color=mygray, cancel_x=True)\n", - " set_boundaries(plt, x_ticks=np.arange(0, 160, 50), x_boundaries=(-10, 160), y_ticks=np.arange(1.4, 1.6, 0.05))\n", - " n += 1\n", - " ax.append(plt.subplot(l_tot, c_tot, n))\n", - " ax[-1].plot(time, number, color=colors[\"myblue\"], linewidth=3)\n", - " complete_panel(ax[-1], r'$t$ (ps)', r'number of bonds', legend=False, axis_color=mygray)\n", - " add_subplotlabels(fig, ax, [\"a\", \"b\"], color=mygray)\n", - " set_boundaries(plt, y_ticks=np.arange(450, 530, 20), x_ticks=np.arange(0, 160, 50), x_boundaries=(-10, 160))\n", - " save_figure(plt, fig, mode, git_path, path_figures, filename)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.6 64-bit", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "vscode": { - "interpreter": { - "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/position-atom-dark.png b/docs/sphinx/source/tutorials/figures/mdanalysis/mdanalysis-tutorial/position-atom-dark.png deleted file mode 100644 index 1566aea46..000000000 Binary files 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image in vacuum - :height: 250 - :align: right - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/CNT_light.webp - :alt: carbon nanotube image in vacuum - :height: 250 - :align: right - :class: only-light - -.. container:: abstract - - The objective of this tutorial is to impose the deformation - of a carbon nanotube (CNT) using LAMMPS. - -.. container:: abstract - - In this tutorial, a small carbon nanotube (CNT) is simulated - within an empty box using LAMMPS. An external - force is imposed on the CNT, and its deformation is measured over time. - -.. container:: abstract - - To illustrate the difference between classical and reactive force fields, - this tutorial is divided into two parts. Within the first part, a classical - force field is used and the bonds between the atoms of the CNT are - unbreakable. Within the second part, a reactive force field - (named AIREBO :cite:`stuart2000reactive`) is used, allowing for the breaking - of chemical bonds when the CNT undergoes strong deformation. - -.. include:: ../../non-tutorials/recommand-lj.rst - -.. include:: ../../non-tutorials/cite.rst - -.. include:: ../../non-tutorials/2Aug2023.rst - -Unbreakable bonds -================= - -.. container:: justify - - With most classical molecular dynamics force fields, the chemical bonds - between the atoms are set at the start of the simulation. Regardless of the - forces applied to the atoms during the simulations, the bonds remain intact. - The bonds between neighbor atoms typically consist of springs with - given equilibrium distances :math:`r_0` and a constant :math:`k_b`: - :math:`U_b = k_b \left( r - r_0 \right)^2`. - Additionally, angular and dihedral constraints are usually applied to maintain - the relative orientations of neighbor atoms. - -Create topology with VMD ------------------------- - -.. container:: justify - - The first part of this tutorial is dedicated to creating - the initial topology with VMD. You can skip this part by - downloading directly the CNT topology |download_cnt_molecular_data|, - and continue with the LAMMPS part of the tutorial. - -.. |download_cnt_molecular_data| raw:: html - - here - -.. admonition:: Why use a preprocessing tool? - :class: info - - When the system has a complex topology, like is the case of a CNT, - it is better to use an external preprocessing tool to create it as it would be - difficult (yet not impossible) to place the atoms in their correct position - using only LAMMPS commands. Many preprocessing tools exist, see - this |prepross| on the LAMMPS website. - -.. |prepross| raw:: html - - non-exhaustive list - -.. container:: justify - - Open VMD, go to Extensions, Modeling, and then Nanotube Builder. - A window named Carbon Nanostructures opens up, allowing us to choose - between generating a sheet or a nanotube, made either of graphene or - of Boron Nitride (BN). For this tutorial, let us generate a carbon nanotube. - Keep all default values, and click on *Generate Nanotube*. - -.. container:: justify - - At this point, this is not a molecular dynamics simulation, - but a cloud of unconnected dots. In the VMD terminal, set the - box dimensions by typing the following commands in the VMD terminal: - -.. code-block:: bw - - molinfo top set a 80 - molinfo top set b 80 - molinfo top set c 80 - -.. container:: justify - - The values of 80 in each direction have been chosen - so that the box is much larger than the carbon nanotube. - -.. container:: justify - - To generate the initial LAMMPS data file, let us use *Topotools*: - to generate the LAMMPS data file, enter the following command - in the VMD terminal: - -.. code-block:: bw - - topo writelammpsdata cnt_molecular.data molecular - -.. container:: justify - - Here *molecular* refers to the LAMMPS *atom_style*, and - *cnt_molecular.data* is the name of the file. - -.. admonition:: About TopoTools - :class: info - - *Topotools* deduces the location of bonds, angles, - dihedrals, and impropers from the respective positions of the atoms, - and generates a *.data* file that can be read by LAMMPS :cite:`kohlmeyer2017topotools`. - More details about *Topotools* can be found on the - personal page of |Axel_webpage|. - -.. |Axel_webpage| raw:: html - - Axel Kohlmeyer - -.. container:: justify - - The parameters of the constraints (bond length, - dihedral coefficients, etc.) will be given later. - A new file named *cnt_molecular.data* has been created, it starts - like that: - -.. code-block:: lammps - - 700 atoms - 1035 bonds - 2040 angles - 4030 dihedrals - 670 impropers - 1 atom types - 1 bond types - 1 angle types - 1 dihedral types - 1 improper types - -40.000000 40.000000 xlo xhi - -40.000000 40.000000 ylo yhi - -12.130411 67.869589 zlo zhi - (...) - -.. container:: justify - - The *cnt_molecular.data* file contains information - about the positions of the carbon atoms, as well as the - identity of the atoms that are linked by *bonds*, *angles*, *dihedrals*, - and *impropers* constraints. - -.. container:: justify - - Save the *cnt_molecular.data* file in a folder named *unbreakable-bonds/*. - -The LAMMPS input ----------------- - -.. container:: justify - - Create a new text file within *unbreakable-bonds/* and name - it *input.lammps*. Copy the following lines into it: - -.. code-block:: lammps - - variable T equal 300 - - units real - atom_style molecular - boundary f f f - pair_style lj/cut 14 - - bond_style harmonic - angle_style harmonic - dihedral_style opls - improper_style harmonic - - special_bonds lj 0.0 0.0 0.5 - - read_data cnt_molecular.data - -.. container:: justify - - The chosen unit system is *real* (therefore distances are in Ångstrom and time in femtosecond), - the *atom_style* is molecular (therefore atoms are dots that can be bonded with each other), - and the boundary conditions are fixed. The boundary conditions - do not matter here, as the box boundaries were placed far from the CNT. - -.. container:: justify - - Just like in :ref:`lennard-jones-label`, - the pair style is *lj/cut* (i.e. a Lennard-Jones potential - with a short-range cutoff) with parameter 14, which means that only the atoms - closer than 14 Ångstroms from each other interact through a Lennard-Jones - potential. - -.. container:: justify - - The *bond_style*, *angle_style*, *dihedral_style*, and *improper_style* commands specify the - different potentials used to restrain the relative positions of the - atoms. For more details about the potentials used here, you can have a look - at the LAMMPS website, see for example - the page of the |OPLS| :cite:`jorgensen1988opls`. - -.. |OPLS| raw:: html - - OPLS dihedral style - -.. container:: justify - - The last command, *read_data*, imports the *cnt_molecular.data* file - previously generated with VMD, which contains - information about the box size, atom positions, etc. - -.. admonition:: About interaction between neighbors atoms - :class: info - - Atoms connected by a bond do not typically interact through - Lennard-Jones interaction. Therefore, atoms that are - bounded must be excluded from the Lennard-Jones potential calculation. - Here, this is done by the *special_bonds* command. The three numbers of the - *special_bonds* command are weighting factors for the - Lennard-Jones interaction between atoms connected by a bond - (respectively directly bounded :math:`C-C`, separated by two bonds :math:`C-C-C`, - and separated by three bonds :math:`C-C-C-C`). For instance, the - first weighting factor, with a value of 0, imposes that two atoms connected - by a bond do not interact through a Lennard-Jones potential (therefore - they only interact through the harmonic potential that bonds the atoms - of the graphene). - -.. container:: justify - - We need to specify the parameters of both bonded and - non-bonded potentials. Here, the parameters are taken from the OPLS-AA - (Optimised Potentials for Liquid Simulations-All-Atom) force - field :cite:`jorgensenDevelopmentTestingOPLS1996`. - Create a new text file in the *unbreakable-bonds/* - folder and name it *parm.lammps*. Copy the following lines into it: - -.. code-block:: lammps - - pair_coeff 1 1 0.066 3.4 - bond_coeff 1 469 1.4 - angle_coeff 1 63 120 - dihedral_coeff 1 0 7.25 0 0 - improper_coeff 1 5 180 - -.. container:: justify - - The *pair_coeff* command sets the parameters for the non-bonded Lennard-Jones - interaction :math:`\epsilon_{11} = 0.066 \, \text{kcal/mol}` - and :math:`\sigma_{11} = 3.4 \, \text{Å}` for the only type of atom of the - simulation; the carbon atom of type 1. - -.. container:: justify - - The *bond_coeff* provides the equilibrium distance :math:`r_0= 1.4 \, \text{Å}` as - well as the spring constant :math:`k_b = 469 \, \text{kcal/mol/Å}^2` for the harmonic - potential imposed between two neighboring carbon atoms, - where the potential is :math:`U_b = k_b ( r - r_0)^2`. The - *angle_coeff* gives the equilibrium angle :math:`\theta_0` and - constant for the potential between three neighbor atoms : - :math:`U_\theta = k_\theta ( \theta - \theta_0)^2`. The *dihedral_coeff* - and *improper_coeff* gives the potential for the constraints - between 4 atoms. - -.. container:: justify - - The file *parm.lammps* is included in the simulation by adding the - following line into the *input.lammps* file: - -.. code-block:: lammps - - include parm.lammps - -Prepare the initial state -------------------------- - -.. container:: justify - - Before starting the molecular dynamics simulation, let us make sure that we - start from a clean initial state by recentering the CNT at the origin (0, 0, 0). - In addition, let us make sure that the box boundaries are symmetric with - respect to (0, 0, 0), which is not initially the case, as seen in *cnt_molecular.data*: - -.. code-block:: lammps - - -40.000000 40.000000 xlo xhi - -40.000000 40.000000 ylo yhi - -12.130411 67.869589 zlo zhi - -.. container:: justify - - Let us recenter the CNT by adding the following lines - to *input.lammps*: - -.. code-block:: lammps - - group carbon_atoms type 1 - variable carbon_xcm equal -1*xcm(carbon_atoms,x) - variable carbon_ycm equal -1*xcm(carbon_atoms,y) - variable carbon_zcm equal -1*xcm(carbon_atoms,z) - displace_atoms carbon_atoms & - move ${carbon_xcm} ${carbon_ycm} ${carbon_zcm} - -.. container:: justify - - The first command includes all the atoms of type 1 - (i.e. all the atoms here) in a group named *carbon_atoms*. - The 3 variables, *carbon_xcm*, *carbon_ycm*, and *carbon_zcm* - are used to measure the current position of the group *carbon_atoms* along - all 3 directions, respectively. Then, the *displace_atoms* - command moves the group *carbon_atoms*, ensuring that its center of mass - is located at the origin (0, 0, 0). - -.. container:: justify - - Let us also change the box boundaries by adding the following line into *input.lammps*: - -.. code-block:: lammps - - change_box all x final -40 40 y final -40 40 z final -40 40 - -.. admonition:: Note - :class: info - - Such a cleaner and more symmetrical initial state can simplify - future data analysis, but won't make any difference to - the molecular dynamics. - -.. container:: justify - - A displacement will be imposed on the edges of the CNT. To do so, let us isolate the - atoms from the two edges and place them into groups named *rtop* - and *rbot*, respectively. Add the following lines into *input.lammps*: - -.. code-block:: lammps - - variable zmax equal bound(carbon_atoms,zmax)-0.5 - variable zmin equal bound(carbon_atoms,zmin)+0.5 - region rtop block INF INF INF INF ${zmax} INF - region rbot block INF INF INF INF INF ${zmin} - region rmid block INF INF INF INF ${zmin} ${zmax} - -.. container:: justify - - The variable :math:`z_\mathrm{max}` corresponds to - the coordinate of the last atoms along :math:`z` minus 0.5 - Ångstroms, and :math:`z_\mathrm{min}` to the coordinate of - the first atoms along :math:`z` plus 0.5 Ångstroms. Then, three - regions are defined and correspond respectively to: :math:`z < z_\mathrm{min}`, - (*rbot*, for region bottom) - :math:`z_\mathrm{min} > z > z_\mathrm{max}` - (*rmid*, for region middle), and - :math:`z > z_\mathrm{max}` - (*rtop*, for region top). - -.. container:: justify - - Finally, let us define 3 groups of atoms - corresponding to the atoms located in each of the three regions, - respectively, by adding to *input.lammps*: - -.. code-block:: lammps - - group carbon_top region rtop - group carbon_bot region rbot - group carbon_mid region rmid - -.. container:: justify - - The atoms of the edges as selected within the *carbon_top* - and *carbon_bot* groups can be represented with a different color. - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/colored-edge-undef-dark.png - :alt: CNT in graphene in vacuum image VMD - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/colored-edge-undef-light.png - :alt: CNT in graphene in vacuum image VMD - :class: only-light - -.. container:: figurelegend - - Figure: CNT with the atoms from the *carbon_mid* group in pink, - and the atoms from the *carbon_top* and the *carbon_bot* groups in white. - -.. container:: justify - - When running a simulation, the number of atoms in each group is printed in - the terminal (and in the *log.lammps* file). Always make sure that the number - of atoms in each group corresponds to what is expected, just like here: - -.. code-block:: bash - - 10 atoms in group carbon_top - 10 atoms in group carbon_bot - 680 atoms in group carbon_mid - -.. container:: justify - - Finally, to start from a less ideal state and create a system with some defects, - let us randomly delete a small fraction of the carbon atoms. - To avoid deleting atoms that are too close to the edges, - let us define a new region name *rdel* that - starts :math:`2\,Å` from the CNT edges. - -.. code-block:: lammps - - variable zmax_del equal ${zmax}-2 - variable zmin_del equal ${zmin}+2 - region rdel block INF INF INF INF ${zmin_del} ${zmax_del} - group rdel region rdel - delete_atoms random fraction 0.02 no rdel NULL 482793 bond yes - -.. container:: justify - - The *delete_atoms* command randomly deletes :math:`2\,\%` of the atoms - from the *rdel* group (i.e. about 10 atoms). - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/colored-edge-deleted-dark.png - :alt: CNT in graphene in vacuum image VMD with deleted atoms - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/colored-edge-deleted-light.png - :alt: CNT in graphene in vacuum image VMD with deleted atoms - :class: only-light - -.. container:: figurelegend - - Figure: CNT with 10 randomly deleted atoms. The 10 deleted atoms were chosen randomly - from the central part of the CNT. - -The molecular dynamics ----------------------- - -.. container:: justify - - Let us specify the thermalization and the dynamics of the - system. Add the following lines into *input.lammps*: - -.. code-block:: lammps - - reset_atoms id sort yes - velocity carbon_mid create ${T} 48455 mom yes rot yes - fix mynve all nve - compute Tmid carbon_mid temp - fix myber carbon_mid temp/berendsen ${T} ${T} 100 - fix_modify myber temp Tmid - -.. container:: justify - - Re-setting the atom IDs is necessary before using the *velocity* command, - this is done by the *reset_atoms* command. - -.. container:: justify - - The *velocity* command gives initial velocities to - the atoms of the middle group *carbon_mid*, ensuring an initial temperature - of 300 K for these atoms with no overall translational momentum, *mom yes*, - nor rotational momentum, *rot yes*. - -.. container:: justify - - The *fix nve* is applied to all atoms so that all atom positions are - recalculated at every step, and a *Berendsen* thermostat is applied to the atoms - of the group *carbon_mid* only :cite:`berendsen1984molecular`. The *fix_modify myber* - ensures that the *fix Berendsen* uses the temperature of the group *carbon_mid* as an - input, instead of the temperature of the whole system. This is necessary - to make sure that the frozen edges won't bias the temperature. Note that the atoms - of the edges do not need a thermostat because their motion will - be restrained, see below. - -.. admonition:: Deal with semi-frozen system - :class: info - - Always be careful when part of a system is frozen, - as is the case here. When some atoms are frozen, the total temperature - of the system is effectively lower than the applied temperature - because the frozen atoms have no thermal motion (their temperature - is therefore :math:`0\,\text{K}`). - -.. container:: justify - - To restrain the motion of the atoms at the edges, let us add the - following commands to *input.lammps*: - -.. code-block:: lammps - - fix mysf1 carbon_top setforce 0 0 0 - fix mysf2 carbon_bot setforce 0 0 0 - velocity carbon_top set 0 0 0 - velocity carbon_bot set 0 0 0 - -.. container:: justify - - The two *setforce* commands cancel the forces applied on the - atoms of the two edges, respectively. The cancellation of the forces - is done at every step, and along all 3 directions of space, :math:`x`, :math:`y`, - and :math:`z`, due to the use of *0 0 0*. The two *velocity* commands - set the initial velocities along :math:`x`, - :math:`y`, and :math:`z` to 0 for the atoms of *carbon_top* - and *carbon_bot*, respectively. - -.. container:: justify - - As a consequence of these last four commands, the atoms of the edges will remain - immobile during the simulation (or at least they would if no other command was - applied to them). - -.. admonition:: On imposing a constant velocity to a system - :class: info - - The *velocity set* commands impose the velocity of a group of atoms at the start of - a run but do not enforce the velocity during the entire simulation. - When *velocity set* is used in combination with *setforce 0 0 0*, - as is the case here, the atoms - won't feel any force during the entire simulation. According to the Newton equation, - no force means no acceleration, meaning that the initial velocity will persist - during the entire simulation, thus producing a constant velocity motion. - -Data extraction ---------------- - -.. container:: justify - - Next, in order to measure the strain and stress suffered by the - CNT, let us extract the distance :math:`L` between - the two edges as well as the force applied on the edges. - -.. code-block:: lammps - - variable L equal xcm(carbon_top,z)-xcm(carbon_bot,z) - fix at1 all ave/time 10 10 100 v_L file output_cnt_length.dat - fix at2 all ave/time 10 10 100 f_mysf1[1] f_mysf2[1] & - file output_edge_force.dat - -.. container:: justify - - Let us also add a command to print the atom coordinates in a - *lammpstrj* file every 1000 steps. - -.. code-block:: lammps - - dump mydmp all atom 1000 dump.lammpstrj - -.. container:: justify - - Finally, let us check the temperature of the non-frozen group over time - by printing it using a *fix ave/time* command: - -.. code-block:: lammps - - fix at3 all ave/time 10 10 100 c_Tmid & - file output_temperature_middle_group.dat - -.. admonition:: About extracting quantity from variable compute or fix - :class: info - - Notice that the values of the force on each edge are - extracted from the two *fix setforce* *mysf1* and *mysf2*, simply by - calling them using *f_*, the same way variables are called - using *v_* and computes are called using *c_*. - -.. container:: justify - - Let us run a small equilibration step to bring the system - to the required temperature before applying any deformation: - -.. code-block:: lammps - - thermo 100 - thermo_modify temp Tmid - - timestep 1.0 - run 5000 - -.. container:: justify - - With the *thermo_modify* command, we specify to LAMMPS that we - want the temperature :math:`T_\mathrm{mid}` to be printed in - the terminal, not the temperature of the entire system - (because of the frozen edges, the temperature of - the entire system is not relevant). - -.. container:: justify - - Let us impose a constant velocity deformation on the CNT by combining - the *velocity set* command with previously defined *fix setforce*. - Add the following lines in the *input.lammps* file, - right after the last *run 5000* command: - -.. code-block:: lammps - - # 2*0.0005 A/fs = 0.001 A/fs = 100 m/s - velocity carbon_top set 0 0 0.0005 - velocity carbon_bot set 0 0 -0.0005 - run 10000 - -.. container:: justify - - The chosen velocity for the deformation is :math:`100\,\text{m/s}`. - The length :math:`L` of the CNT increase linearly over - time for :math:`t > 5\,\text{ps}`, as expected from the imposed constant velocity. - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/length-unbreakable.png - :alt: length of the CNT with time - lammps molecular dynamics - :class: only-light - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/length-unbreakable-dm.png - :alt: length of the CNT with time - lammps molecular dynamics - :class: only-dark - -.. container:: figurelegend - - Figure: Evolution of the length :math:`L` of the CNT with time. - The CNT starts deforming at :math:`t = 5\,\text{ps}`. - -.. container:: justify - - The energy, which can be accessed from the log file, shows a non-linear - increase with time once the deformation starts, - which is expected from the typical dependency of bond energy with - bond distance :math:`U_b = k_b \left( r - r_0 \right)^2`. - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/energy-unbreakable-dm.png - :alt: energy of the CNT with time - lammps molecular dynamics - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/energy-unbreakable.png - :alt: energy of the CNT with time - lammps molecular dynamics - :class: only-light - -.. container:: figurelegend - - Figure: Evolution of the total energy of the system with time. - The CNT starts deforming at :math:`t = 5\,\text{ps}`. - -.. container:: justify - - As always, is it important to ensure that the simulation - behaves as expected by opening the *dump.lammpstrj* file with VMD. - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/colored-edge-def-dark.png - :alt: CNT in graphene in vacuum image VMD before and after deformation - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/colored-edge-def-light.png - :alt: CNT in graphene in vacuum image VMD before and after deformation - :class: only-light - -.. container:: figurelegend - - Figure: CNT before (top) and after (bottom) deformation. See the corresponding |unbreakable_cnt_video|. - -.. |unbreakable_cnt_video| raw:: html - - video - -Breakable bonds -=============== - -.. container:: justify - - When using a classical force field, as we just did, the bonds between the atoms - are non-breakable. Let us perform a similar simulation and deform a small CNT again, - but this time using a reactive force field that allows for the bonds to break - if the applied deformation is large enough. - -Input file initialization -------------------------- - -.. container:: justify - - Create a second folder named *breakable-bonds/* next to *unbreakable-bonds/*, - and create a new input file in it called *input.lammps*. Type into input.lammps*: - -.. code-block:: lammps - - # Initialisation - variable T equal 300 - - units metal - atom_style atomic - boundary p p p - pair_style airebo 2.5 1 1 - -.. container:: justify - - The first difference with the previous part - is the unit system, here *metal* instead of *real*, a choice - that is imposed by the AIREBO force field. A second difference - is the use of the *atom_style atomic* instead of *molecular*, - since no explicit bond information is required with AIREBO. - -.. admonition:: About metal units - :class: info - - With the *metal* units system of LAMMPS, the time is in pico second, - distances are in Ångstrom, and the energy is in eV. - -Adapt the topology file ------------------------ - -.. container:: justify - - Since *bond*, *angle*, and *dihedral* do not need to be explicitly - set when using AIREBO, some small changes need to be made to the - previously generated *.data* file. - -.. container:: justify - - Duplicate the previous file *cnt_molecular.data*, name the copy *cnt_atom.data*, - and place it within *breakable-bonds/*. Then, remove all bond, angle, and dihedral - information from *cnt_atom.data*. Also, remove the second column of the - *Atoms* table, so that the *cnt_atom.data* looks like the following: - -.. code-block:: lammps - - 700 atoms - 1 atom types - -40.000000 40.000000 xlo xhi - -40.000000 40.000000 ylo yhi - -12.130411 67.869589 zlo zhi - - Masses - - 1 12.010700 # CA - - Atoms # atomic - - 1 1 5.162323 0.464617 8.843235 # CA CNT - 2 1 4.852682 1.821242 9.111212 # CA CNT - (...) - -.. container:: justify - - In addition, remove the *Bonds* table that is placed right after the - *Atoms* table (near line 743), as well as the *Angles*, *Dihedrals*, - and *Impropers* tables. The last lines of the file should look like this: - -.. code-block:: lammps - - (...) - 697 1 4.669892 -2.248901 45.824036 # CA CNT - 698 1 5.099893 -0.925494 46.092010 # CA CNT - 699 1 5.162323 -0.464617 47.431896 # CA CNT - 700 1 5.099893 0.925494 47.699871 # CA CNT - -.. container:: justify - - Alternatively, you can also download the file I generate - by clicking |download_CNT.data|. - -.. |download_CNT.data| raw:: html - - here - -Use of AIREBO potential ------------------------ - -.. container:: justify - - Then, let us import the LAMMPS data file, and set the - pair coefficients by adding the following lines into *input.lammps* - -.. code-block:: lammps - - # System definition - read_data cnt_atom.data - pair_coeff * * CH.airebo C - -.. container:: justify - - Here, there is one single atom type. We impose this type - to be carbon by using the letter *C*. - -.. admonition:: Setting AIREBO pair coefficients - :class: info - - In the case of multiple atom types, one has to adapt the *pair_coeff* command. - If there are 2 atom types, and both are carbon, it would read: *pair_coeff * * CH.airebo C C*. - If atoms of type 1 are carbon and atoms of type 2 are hydrogen, then *pair_coeff * * CH.airebo C H*. - -.. container:: justify - - The *CH.airebo* file can be - downloaded by clicking |download_CH.airebo|, - and must be placed within the *breakable-bonds/* folder. - The rest of the *input.lammps* is very similar to the previous one: - -.. |download_CH.airebo| raw:: html - - here - -.. code-block:: lammps - - change_box all x final -40 40 y final -40 40 z final -60 60 - - group carbon_atoms type 1 - variable carbon_xcm equal -1*xcm(carbon_atoms,x) - variable carbon_ycm equal -1*xcm(carbon_atoms,y) - variable carbon_zcm equal -1*xcm(carbon_atoms,z) - displace_atoms carbon_atoms move ${carbon_xcm} ${carbon_ycm} ${carbon_zcm} - - variable zmax equal bound(carbon_atoms,zmax)-0.5 - variable zmin equal bound(carbon_atoms,zmin)+0.5 - region rtop block INF INF INF INF ${zmax} INF - region rbot block INF INF INF INF INF ${zmin} - region rmid block INF INF INF INF ${zmin} ${zmax} - - group carbon_top region rtop - group carbon_bot region rbot - group carbon_mid region rmid - - variable zmax_del equal ${zmax}-2 - variable zmin_del equal ${zmin}+2 - region rdel block INF INF INF INF ${zmin_del} ${zmax_del} - group rdel region rdel - delete_atoms random fraction 0.02 no rdel NULL 482793 - - reset_atoms id sort yes - velocity carbon_mid create ${T} 48455 mom yes rot yes - fix mynve all nve - compute Tmid carbon_mid temp - fix myber carbon_mid temp/berendsen ${T} ${T} 0.1 - fix_modify myber temp Tmid - -.. container:: justify - - Note that a large distance of 120 Ångstroms was used for the box size along - the *z* axis, to allow for larger deformation. In addition, the *change_box* command - was placed before the *displace_atoms* to avoid having the CNT crossing the edge of the box. - -Start the simulation --------------------- - -.. container:: justify - - Here, let us impose a constant velocity deformation using the atoms of one - edge while maintaining the other edge fixed (note that for the unbreakable - CNT, the motion was imposed on the 2 edges). - -.. container:: justify - - First, as an equilibration step, let us set the velocity to 0 - for the atoms of both edges. Let us fully constrain the edges. - Add the following lines into LAMMPS: - -.. code-block:: lammps - - fix mysf1 carbon_bot setforce 0 0 0 - fix mysf2 carbon_top setforce 0 0 0 - velocity carbon_bot set 0 0 0 - velocity carbon_top set 0 0 0 - - variable L equal xcm(carbon_top,z)-xcm(carbon_bot,z) - fix at1 all ave/time 10 10 100 v_L file output_cnt_length.dat - fix at2 all ave/time 10 10 100 f_mysf1[1] f_mysf2[1] & - file output_edge_force.dat - - dump mydmp all atom 1000 dump.lammpstrj - - thermo 100 - thermo_modify temp Tmid - - timestep 0.0005 - run 5000 - -.. container:: justify - - Note the relatively small timestep of :math:`0.0005\,\text{ps}` used. A - reactive force field usually requires a smaller timestep than a classical one. - When running *input.lammps* with LAMMPS, you can see that the - temperature deviates from the target temperature of :math:`300\,\text{K}` - at the start of the equilibration, but that - after a few steps, it reaches the target value: - -.. code-block:: bw - - Step Temp E_pair E_mol TotEng Press - 0 300 -5084.7276 0 -5058.3973 -1515.7017 - 100 237.49462 -5075.4114 0 -5054.5671 -155.05545 - 200 238.86589 -5071.9168 0 -5050.9521 -498.15029 - 300 220.04074 -5067.1113 0 -5047.7989 -1514.8516 - 400 269.23434 -5069.6565 0 -5046.0264 -174.31158 - 500 274.92241 -5068.5989 0 -5044.4696 -381.28758 - 600 261.91841 -5065.985 0 -5042.9971 -1507.5577 - 700 288.47709 -5067.7301 0 -5042.4111 -312.16669 - 800 289.85177 -5066.5482 0 -5041.1086 -259.84893 - 900 279.34891 -5065.0216 0 -5040.5038 -1390.8508 - 1000 312.27343 -5067.6245 0 -5040.217 -465.74352 - (...) - -Launch the deformation ----------------------- - -.. container:: justify - - After equilibration, let us set the velocity to 15 m/s and run for - a longer duration than previously. Add the following lines into - *input.lammps*: - -.. code-block:: lammps - - # 0.15 A/ps = 15 m/s - velocity carbon_top set 0 0 0.15 - run 280000 - -.. container:: justify - - The CNT should break around step 250000. If not, - use a longer run. - -.. container:: justify - - By opening the *lammpstrj* file using VMD, it is possible to observe the - bonds breaking at approximately two-thirds of the simulation. If the bonds - do not break, use a longer run. - -.. |video_lammps_cnt| raw:: html - - this video - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/deformed-dark.png - :alt: carbon nanotube with broken bonds after simulation with LAMMPS and AIREBO - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/deformed-light.png - :alt: carbon nanotube with broken bonds after simulation with LAMMPS and AIREBO - :class: only-light - -.. container:: figurelegend - - Figure: CNT with broken bonds. See the corresponding |breakable_cnt_video|. - -.. |breakable_cnt_video| raw:: html - - video - -.. admonition:: About bonds in VMD - :class: info - - Note that VMD guesses bonds based on the distances - between atoms, and not based on the presence of actual - bonds between atoms in the LAMMPS simulation. Therefore the bonds seen - in VMD when using the *DynamicBonds* representation can be misleading. - -.. container:: justify - - Looking at the evolution of energy again, one can see that the energy is increasing - with the deformation. When bonds break, the energy relaxes abruptly, as can be seen - near $t=210~\text{ps}$ when plotting the evolution of the total energy with time. - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/energy-breakable-dm.png - :alt: energy of the CNT with time - lammps molecular dynamics - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/energy-breakable.png - :alt: energy of the CNT with time - lammps molecular dynamics - :class: only-light - -.. container:: figurelegend - - Figure: Evolution of the total energy of the system with time. - -.. include:: ../../non-tutorials/accessfile.rst - -.. container:: justify - - There is a follow-up to this CNT tutorial as :ref:`mda-label`, - where a post-mortem analysis is performed using Python. - -Going further with exercises -============================ - -.. include:: ../../non-tutorials/link-to-solutions.rst - -Plot the strain-stress curves ------------------------------ - -.. container:: justify - - Adapt the current scripts and extract the strain-stress curves for - the two breakable and unbreakable CNTs: - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/stress-strain-curve-dark.png - :alt: strain strain curve of the CNTs - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/stress-strain-curve-light.png - :alt: strain strain curve of the CNTs - :class: only-light - -.. container:: figurelegend - - Figure: Strain-stain curves for the two CNTs, breakable and unbreakable. - -Solve the flying ice cube artifact ----------------------------------- - -.. container:: justify - - The flying ice cube effect is one of the most famous artifacts of - molecular simulations :cite:`wong2016good`. - Download this seemingly simple |input_flying_cube|, which is a simplified - version of the input from the first part of the tutorial. - Run the input with this |data_flying_cube| file - and this |parm_flying_cube| file. - -.. |input_flying_cube| raw:: html - - input - -.. |data_flying_cube| raw:: html - - data - -.. |parm_flying_cube| raw:: html - - parameter - -.. container:: justify - - When you run this simulation using LAMMPS, you should see that the temperature is - very close to :math:`300\,\text{K}`, as expected. - -.. code-block:: bash - - Step Temp E_pair E_mol TotEng Press - 0 327.4142 589.20707 1980.6012 3242.2444 60.344754 - 1000 300.00184 588.90015 1980.9013 3185.9386 51.695282 - (...) - -.. container:: justify - - However, if you look at the system using VMD, the atoms are not moving. - -.. container:: justify - - Can you identify the origin of the issue, and fix the input? - -Insert gas in the carbon nanotube ---------------------------------- - -.. container:: justify - - Modify the input from the unbreakable CNT, and add atoms of argon - within the CNT. - -.. container:: justify - - Use the following *pair_coeff* for the argon, - and a mass of *39.948*: - -.. code-block:: lammps - - pair_coeff 2 2 0.232 3.3952 - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/CNT-gas-dark.png - :alt: CNT with Argon modeled in LAMMPS - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/CNT-gas-light.png - :alt: CNT with Argon modeled in LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: Argon atoms in a CNT. See the corresponding |gas_cnt_video|. - -.. |gas_cnt_video| raw:: html - - video - -Make a membrane of CNTs ------------------------ - -.. container:: justify - - Replicate the CNT along the *x* - and *y* direction, and equilibrate the system to - create an infinite membrane made of multiple CNTs. - -.. container:: justify - - Apply a shear deformation along *xy*. - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/membrane-dark.png - :alt: deformed membrane of CNTs - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/membrane-light.png - :alt: deformed membrane of CNTs - :class: only-light - -.. container:: figurelegend - - Figure: Multiple carbon nanotubes forming a membrane. - -.. admonition:: Hint - :class: info - - The box must be converted to triclinic to support deformation - along *xy*. - diff --git a/docs/sphinx/source/tutorials/level1/lennard-jones-fluid.rst b/docs/sphinx/source/tutorials/level1/lennard-jones-fluid.rst deleted file mode 100644 index 3bed67be5..000000000 --- a/docs/sphinx/source/tutorials/level1/lennard-jones-fluid.rst +++ /dev/null @@ -1,1177 +0,0 @@ -.. _lennard-jones-label: - -Lennard-Jones fluid -******************* - -.. container:: hatnote - - The very basics of LAMMPS through a simple example - -.. figure:: ../figures/level1/lennard-jones-fluid/lennard-jones-fluid-avatar-dark.webp - :alt: The binary mixture simulated during Tutorial 1. The atoms of type 1 are - represented as small green spheres and the atoms of type 2 as large blue spheres. - :height: 250 - :align: right - :class: only-dark - -.. figure:: ../figures/level1/lennard-jones-fluid/lennard-jones-fluid-avatar-light.webp - :alt: The binary mixture simulated during Tutorial 1. The atoms of type 1 are - represented as small green spheres and the atoms of type 2 as large blue spheres. - :height: 250 - :align: right - :class: only-light - -.. container:: abstract - - The objective of this tutorial is to perform the simulation of a binary - fluid using LAMMPS. - -.. container:: abstract - - The system is a Lennard-Jones fluid composed of neutral particles with two - different diameters, contained within a cubic box with periodic boundary conditions - In this tutorial, the temperature of the system is maintained using a Langevin - thermostat :cite:`schneider1978molecular`, and basic quantities are extracted - from the system, including the potential and kinetic energies. - -.. container:: abstract - - This tutorial illustrates several key ingredients of molecular dynamics - simulations, such as system initialization, energy minimization, integration - of the equations of motion, and trajectory visualization. - -.. include:: ../../non-tutorials/cite.rst -.. include:: ../../non-tutorials/2Aug2023.rst - -My first input -============== - -.. container:: justify - - To run a simulation using LAMMPS, one needs to write a series of commands - in an input script. For clarity, the input scripts written for this first - tutorial will be divided into five categories which we are going to fill up - one by one. - -.. container:: justify - - Create a folder, call it *my-first-input/*, and then create a blank - text file in it called *input.lammps*. Copy the following lines - in *input.lammps*, where a line starting with a hash symbol (#) - is a comment ignored by LAMMPS: - -.. code-block:: lammps - - # PART A - ENERGY MINIMIZATION - # 1) Initialization - # 2) System definition - # 3) Simulation settings - # 4) Visualization - # 5) Run - -.. container:: justify - - These five categories are not required in every input script, and should not - necessarily be in that exact order. For instance, parts 3 and 4 could be - inverted, or part 4 could be omitted. Note however that LAMMPS reads input - files from top to bottom, therefore the *Initialization* and *System definition* - categories must appear at the top of the input, and the *Run* category at - the bottom. - -System initialization ---------------------- - -.. container:: justify - - In the first section of the script, called *Initialization*, - let us indicate to LAMMPS the most basic information - about the simulation, such as: - - - the conditions at the boundaries of the box (e.g. periodic or non-periodic), - - the type of atoms (e.g. uncharged single dots or spheres with angular velocities). - - Enter the following lines in *input.lammps*: - -.. code-block:: lammps - - # 1) Initialization - units lj - dimension 3 - atom_style atomic - pair_style lj/cut 2.5 - boundary p p p - -.. container:: justify - - The first line, *units lj*, indicates that we want to use the unit system - called *LJ* (Lennard-Jones), in which all quantities are unitless. - -.. admonition:: About Lennard-Jones (LJ) units - :class: info - - Lennard-Jones (LJ) units are a dimensionless system of units. LJ units are - often used in molecular simulations and theoretical calculations. When using - LJ units: - - - energies are expressed in units of :math:`\epsilon`, where :math:`\epsilon` - is the depth of the potential of the LJ interaction, - - distances are expressed in units of :math:`\sigma`, where :math:`\sigma` is - the distance at which the particle-particle potential energy is zero, - - masses are expressed in units of the atomic mass :math:`m`. - - All the other quantities are normalized by a combination of :math:`\epsilon`, :math:`\sigma`, - and :math:`m`. For instance, time is expressed in units of :math:`\sqrt{ \epsilon / m \sigma^2}`. - Find details on the |LAMMPS_units|. - -.. |LAMMPS_units| raw:: html - - LAMMPS website - -.. container:: justify - - The second line, *dimension 3*, indicates that the simulation - is 3D. The third line, *atom_style atomic*, that the *atomic* style - will be used, therefore each atom is just a dot with a mass. - -.. admonition:: About the atom style - :class: info - - While we are keeping things as simple as possible in this tutorial, - different *atom_style* will be used in the following tutorials. - Notably, these other atom styles will allow us to create molecules, - i.e. atoms with partial charges and chemical bonds. You can find the complete list - of implemented atom styles from the |atom style page|. - -.. |atom style page| raw:: html - - atom style page - -.. container:: justify - - The fourth line, *pair_style lj/cut 2.5*, indicates that atoms - will be interacting through a Lennard-Jones potential with - a cut-off equal to :math:`r_c = 2.5` (unitless) - :cite:`wang2020lennard,fischer2023history`: - -.. math:: - - E_{ij} (r) = 4 \epsilon_{ij} \left[ \left( \dfrac{\sigma_{ij}}{r} \right)^{12} - - \left( \dfrac{\sigma_{ij}}{r} \right)^{6} \right], ~ \text{for} ~ r < r_c, - -.. container:: justify - - where :math:`r` is the inter-particle distance, - :math:`\epsilon_{ij}` is the depth of potential well that sets the interaction strength, and - :math:`\sigma_{ij}` is the distance parameter or particle effective size. - Here, the indexes *ij* refer to the particle types *i* and *j*. - -.. admonition:: About Lennard-Jones potential - :class: info - - The Lennard-Jones potential offers a simplified representation that captures - the fundamental aspects of interactions among atoms. It depicts a scenario where two - particles exhibit repulsion at extremely close distances, attraction at moderate - distances, and no interaction at infinite separation. The repulsive part of the - Lennard-Jones potential (i.e. the term :math:`\propto r^{-12}`) is associated - with the Pauli exclusion principle. The attractive part (i.e. the term - in :math:`\propto - r^{-6}`) is linked with the London dispersion forces. - -.. container:: justify - - The last line, *boundary p p p*, indicates that the periodic boundary - conditions will be used along all three directions of space (the 3 *p* stand - for *x*, *y*, and *z*, respectively). - -.. container:: justify - - At this point, the *input.lammps* is a LAMMPS input script that does nothing. - You can run it using LAMMPS to verify that the *input* contains - no mistake by typing the following command in the terminal - from the *my-first-input/* folder: - -.. code-block:: bw - - lmp -in input.lammps - -.. container:: justify - - Here *lmp* is linked to my compiled LAMMPS version. - Running the previous command should return: - -.. code-block:: bw - - LAMMPS (2 Aug 2023 - Update 1) - Total wall time: 0:00:00 - -.. container:: justify - - In case there is a mistake in the input script, for example, if - *atom_stile* is written instead of *atom_style*, LAMMPS - gives you an explicit warning: - -.. code-block:: bw - - LAMMPS (2 Aug 2023 - Update 1) - ERROR: Unknown command: atom_stile atomic (src/input.cpp:232) - Last command: atom_stile atomic - -System definition ------------------ - -.. container:: justify - - Let us fill the *System definition* category of the input script: - -.. code-block:: lammps - - # 2) System definition - region simulation_box block -20 20 -20 20 -20 20 - create_box 2 simulation_box - create_atoms 1 random 1500 341341 simulation_box - create_atoms 2 random 100 127569 simulation_box - -.. container:: justify - - The first line, *region simulation_box (...)*, creates a region - named *simulation_box* that is a block (i.e. a rectangular cuboid) that - extends from -20 to 20 (no unit) along all 3 directions of space. - -.. container:: justify - - The second line, *create_box 2 simulation_box*, creates a simulation box based on - the region *simulation_box* with *2* types of atoms. - -.. container:: justify - - The third line, *create_atoms (...)* creates 1500 atoms of type 1 - randomly within the region *simulation_box*. The integer *341341* is a - seed that can be changed in order to create different - initial conditions for the simulation. The fourth line - creates 100 atoms of type 2. - -.. container:: justify - - If you run LAMMPS, you should see the following information in the terminal: - -.. code-block:: bw - - (...) - Created orthogonal box = (-20 -20 -20) to (20 20 20) - (...) - Created 1500 atoms - (...) - Created 100 atoms - (...) - -.. container:: justify - - From what is printed in the terminal, it is clear that - LAMMPS correctly interpreted the commands, and first created - the box with desired dimensions, then 1500 atoms, and then 100 - atoms. - -Simulation Settings -------------------- - -.. container:: justify - - Let us fill the *Simulation Settings* category section of - the *input* script: - -.. code-block:: lammps - - # 3) Simulation settings - mass 1 1 - mass 2 1 - pair_coeff 1 1 1.0 1.0 - pair_coeff 2 2 0.5 3.0 - -.. container:: justify - - The two first commands, *mass (...)*, attribute a mass - equal to 1 (unitless) to both atoms of type 1 and 2. - Alternatively, one could have written - these two commands into one single line: *mass * 1*, - where the star symbol means *all* the atom types of the simulation. - -.. container:: justify - - The third line, *pair_coeff 1 1 1.0 1.0*, sets the Lennard-Jones - coefficients for the interactions between atoms of type 1, - respectively the energy parameter :math:`\epsilon_{11} = 1.0` and the distance - parameter :math:`\sigma_{11} = 1.0`. - -.. container:: justify - - Similarly, the last line sets the Lennard-Jones coefficients for - the interactions between atoms of type 2, :math:`\epsilon_{22} = 0.5`, - and :math:`\sigma_{22} = 3.0`. - -.. admonition:: About cross parameters - :class: info - - By default, LAMMPS calculates the cross coefficients between the different atom types - using geometric average: :math:`\epsilon_{ij} = \sqrt{\epsilon_{ii} \epsilon_{jj}}`, - :math:`\sigma_{ij} = \sqrt{\sigma_{ii} \sigma_{jj}}`. In the present case, - and even without specifying it explicitly, we thus have: - - - :math:`\epsilon_{12} = \sqrt{1.0 \times 0.5} = 0.707`, and - - :math:`\sigma_{12} = \sqrt{1.0 \times 3.0} = 1.732`. - - When necessary, cross-parameters can be explicitly specified - by adding the following line into the input file: *pair_coeff 1 2 0.707 1.732*. - This can be used for instance to increase the attraction between particles - of type 1 and 2, without affecting the interactions between particles of the same type. - - Note that the arithmetic rule, also known as - Lorentz-Berthelot rule :cite:`lorentz1881ueber,berthelot1898melange`, where - :math:`\epsilon_{ij} = \sqrt{\epsilon_{ii} \epsilon_{jj}}`, - :math:`\sigma_{ij} = (\sigma_{ii}+\sigma_{jj})/2`, is more common than the - geometric rule. However, neither the geometric nor the arithmetic rules are - based on rigorous arguments, so here the geometric rule will do just fine. - -.. container:: justify - - Due to the chosen Lennard-Jones parameters, the two types of particles - are given different effective diameters, as can be seen by plotting - :math:`E_{11} (r)`, - :math:`E_{12} (r)`, - and :math:`E_{22} (r)`. - -.. figure:: ../figures/level1/lennard-jones-fluid/lennard-jones.png - :alt: Lennard Jones potential - :class: only-light - :name: fig-lennard-jones - -.. figure:: ../figures/level1/lennard-jones-fluid/lennard-jones-dm.png - :alt: Lennard Jones potential - :class: only-dark - -.. container:: figurelegend - - Figure: The Lennard-Jones potential :math:`E_{ij} (r)` - as a function of the inter-particle distance, where - :math:`i, j = 1 ~ \text{or} ~ 2`. This figure was generated using Python - with Matplotlib Pyplot, and the notebook can be accessed |lennard-jones-pyplot.ipynb|. - The Pyplot parameters used for all figures can be accessed in a |pyplot-perso|. - -.. |lennard-jones-pyplot.ipynb| raw:: html - - from Github - -.. |pyplot-perso| raw:: html - - dedicated repository - -Energy minimization -------------------- - -.. container:: justify - - The system is now fully parametrized. Let us fill the two last remaining sections - by adding the following lines into *input.lammps*: - -.. code-block:: lammps - - # 4) Visualization - thermo 10 - thermo_style custom step temp pe ke etotal press - - # 5) Run - minimize 1.0e-4 1.0e-6 1000 10000 - -.. container:: justify - - The *thermo* command asks LAMMPS to print - thermodynamic information (e.g. temperature, energy) in the - terminal every given number of steps, here 10 steps. - The *thermo_style custom* requires LAMMPS to print - the system temperature (*temp*), potential energy (*pe*), - kinetic energy (*ke*), total energy (*etotal*), - and pressure (*press*). Finally, the *minimize* command - instructs LAMMPS to perform an energy minimization of the system. - -.. admonition:: About energy minimization - :class: info - - An energy minimization procedure consists of adjusting the coordinates of - the atoms that are too close to each other until one of the stopping - criteria is reached. By default, LAMMPS uses the conjugate - gradient (CG) algorithm :cite:`hestenes1952methods` (see all the other - implemented methods on the |min_style| page), which runs - until one of the following criteria is reached: - - - The change in energy between two iterations is less than 1.0e-4. - - The maximum force between two atoms in the system is lower than 1.0e-6. - - The maximum number of iterations is 1000. - - The maximum number of times the force and the energy have been evaluated is 10000. - -.. |min_style| raw:: html - - min style - -.. container:: justify - - Now running the simulation, we can see how the thermodynamic - variables evolve as the simulation progresses: - -.. code-block:: bw - - Step Temp PotEng KinEng TotEng Press - 0 0 78840982 0 78840982 7884122 - 10 0 169.90532 0 169.90532 17.187291 - 20 0 -0.22335386 0 -0.22335386 -0.0034892297 - 30 0 -0.31178296 0 -0.31178296 -0.0027290466 - 40 0 -0.38135002 0 -0.38135002 -0.0016419218 - 50 0 -0.42686621 0 -0.42686621 -0.0015219081 - 60 0 -0.46153953 0 -0.46153953 -0.0010659992 - 70 0 -0.48581568 0 -0.48581568 -0.0014849169 - 80 0 -0.51799572 0 -0.51799572 -0.0012995545 - (...) - -.. container:: justify - - These lines give us information about - the progress of the energy minimization. First, at the start - of the simulation (Step 0), the energy in the system is - huge: 78840982 (unitless). This was expected because - the atoms have been created at random positions within the - simulation box and some of them are probably overlapping, - resulting in a large initial energy which is the consequence - of the repulsive part of the Lennard-Jones interaction - potential. As the energy minimization progresses, the energy - rapidly decreases and reaches a negative value, indicating that the atoms have been - displaced at reasonable distances from each other. - -.. admonition:: On the temperature during energy minimization - :class: info - - As a side note, during energy minimization both temperature and kinetic energy remain equal to - their initial values of 0. This is expected as the conjugate gradient - algorithm only affects the positions of the particles based on the - forces between them, without affecting their velocities. - -.. container:: justify - - Other useful information has been printed in the terminal, for example, LAMMPS - tells us that the first of the four criteria to be satisfied was the energy: - -.. code-block:: bw - - Minimization stats: - Stopping criterion = energy tolerance - -Molecular dynamics ------------------- - -.. container:: justify - - The system is now ready. Let us continue by completing the input script and - adding commands to perform a molecular dynamics simulation, starting from the - final state of the previous energy minimization step. - -.. admonition:: Background Information -- What is molecular dynamics? - :class: info - - Molecular dynamics (MD) is based on the numerical solution of the Newtonian - equations of motion for every atom :math:`i`, - - .. math:: - - \sum_{j \ne i} \boldsymbol{F}_{ji} = m_i \times \boldsymbol{a}_i, - - where :math:`\sum` is the sum over all the atoms other than :math:`i`, - :math:`\boldsymbol{F}_{ji}` the force between the atom pairs :math:`j-i`, - :math:`m_i` the mass of atom :math:`i`, and :math:`\boldsymbol{a}_i` its acceleration. - The Newtonian equations are solved at every step to predict the - evolution of the positions and velocities of atoms and molecules over - time. Then, the velocity and position of each atom are updated according to the - calculated acceleration, typically using the Verlet algorithm, or similar. - More information can be found in Refs. :cite:`allen2017computer,frenkel2023understanding`. - -.. container:: justify - - In the same input script, after the *minimization* command, add the following - lines: - -.. code-block:: lammps - - # PART B - MOLECULAR DYNAMICS - # 4) Visualization - thermo 50 - -.. container:: justify - - Since LAMMPS reads the input from top to bottom, these lines will be - executed after the energy minimization. There is no need to re-initialize - or re-define the system. The *thermo* command is called a second time within - the same input, so the previously entered value of 10 will be replaced by - the value of 50 as soon as *PART B* starts. - -.. container:: justify - - Then, let us add a second *Run* section: - -.. code-block:: lammps - - # 5) Run - fix mynve all nve - fix mylgv all langevin 1.0 1.0 0.1 1530917 - timestep 0.005 - run 10000 - -.. container:: justify - - The *fix nve* is used to update the positions and the velocities of the - atoms in the group *all* at every step. The group *all* is a default group - that contains every atom. - -.. container:: justify - - The second fix applies a Langevin thermostat to the atoms of the group - *all*, with a desired initial temperature of 1.0 (unitless), and a final - temperature of 1.0 as well :cite:`schneider1978molecular`. A *damping* parameter - of 0.1 is used. The *damping* parameter determines how rapidly the temperature - is relaxed to its desired value. The number *1530917* is a seed, you can - change it to perform statistically independent simulations. Finally, the last - two lines set the value of the *timestep* and the number of steps for the *run*, - respectively, corresponding to a total duration of 50 (unitless). - -.. admonition:: What is a fix? - :class: info - - In LAMMPS, a *fix* is a command that performs specific tasks during a simulation, - such as imposing constraints, applying forces, or modifying particle properties. - Other LAMMPS-specific terms are defined in the :ref:`glossary-label`. - -.. container:: justify - - After running the simulation, similar lines should appear in the terminal: - -.. code-block:: bw - - Step Temp PotEng KinEng TotEng Press - 388 0 -0.95476642 0 -0.95476642 -0.000304834 - 400 0.68476875 -0.90831467 1.0265112 0.11819648 0.023794293 - 500 0.97168188 -0.56803405 1.4566119 0.88857783 0.02383215 - 600 1.0364167 -0.44295618 1.5536534 1.1106972 0.027985679 - 700 1.010934 -0.39601767 1.5154533 1.1194356 0.023064983 - 800 0.98641731 -0.37866057 1.4787012 1.1000406 0.023131153 - 900 1.0074571 -0.34951264 1.5102412 1.1607285 0.023520785 - (...) - -.. container:: justify - - The second column shows that the temperature *Temp* starts from 0, but rapidly - reaches the requested value and stabilize itself near :math:`T=1`. - -.. container:: justify - - From what has been printed in the *log* file, one can plot the potential - energy (:math:`p_\text{e}`) and the kinetic energy (:math:`k_\text{e}`) of - the system over time. The potential energy, :math:`p_\text{e}`, rapidly - decreases during energy minimization. Then, after the molecular dynamics - simulation starts, :math:`p_\text{e}` increases until it reaches a plateau - value of about -0.25. The kinetic energy, :math:`k_\text{e}`, is equal to - zero during energy minimization and then increases during molecular - dynamics until it reaches a plateau value of about 1.5. - -.. figure:: ../figures/level1/lennard-jones-fluid/energy.png - :alt: Result tutorial molecular dynamics simulation: Energy plot over time - :class: only-light - -.. figure:: ../figures/level1/lennard-jones-fluid/energy-dm.png - :alt: Result tutorial molecular dynamics simulation: Energy plot over time - :class: only-dark - -.. container:: figurelegend - - Figure: a) Potential energy (:math:`p_\text{e}`) of the binary mixture as a function - of the time :math:`t`. b) Kinetic energy (:math:`k_\text{e}`) as a function of :math:`t`. - -Trajectory visualization ------------------------- - -.. container:: justify - - The simulation is running well, but we would like to - visualize the trajectories of the atoms. To do so, we first need - to print the positions of the atoms in a file at a regular interval. - -.. container:: justify - - Add the following command to the *input.lammps* file, in the *Visualization* - section of *PART B*: - -.. code-block:: lammps - - dump mydmp all atom 100 dump.lammpstrj - -.. container:: justify - - Run the *input.lammps* using LAMMPS again. A file named *dump.lammpstrj* - must appear within *my-first-input/*. A *.lammpstrj* file can - be opened using VMD. With Ubuntu/Linux, you can simply execute in the terminal: - -.. code-block:: bw - - vmd dump.lammpstrj - -.. container:: justify - - Otherwise, you can open VMD and import the *dump.lammpstrj* - file manually using *File -> New molecule*. - -.. container:: justify - - By default, you should see a cloud of lines, but you can improve the - representation (see this :ref:`vmd-label` for basic instructions). - -.. figure:: ../figures/level1/lennard-jones-fluid/first-input-light.png - :alt: binary fluid simulated by LAMMPS and visualized with VMD - :class: only-light - -.. figure:: ../figures/level1/lennard-jones-fluid/first-input-dark.png - :alt: binary fluid simulated by LAMMPS and visualized with VMD - :class: only-dark - -.. container:: figurelegend - - Figure: View of a slice of the system using VMD, with both - types of atoms represented as spheres. See the corresponding |my_first_input_video|. - -.. |my_first_input_video| raw:: html - - video - -Improving the script -==================== - -.. container:: justify - - Let us improve the input script and perform slightly more advanced operations, - such as imposing a specific initial positions to the atoms, and restarting the simulation - from a previously saved configuration. - -Control the initial atom positions ----------------------------------- - -.. container:: justify - - Create a new folder next to *my-first-input/*, and call it *improved-input/*. - Then, create a new input file within *improved-input/* and call it - *input.min.lammps*. - -.. container:: justify - - Similarly to what has been done previously, copy the following lines - into *input.min.lammps*: - -.. code-block:: lammps - - # 1) Initialization - units lj - dimension 3 - atom_style atomic - pair_style lj/cut 2.5 - boundary p p p - -.. container:: justify - - To create the atoms of types 1 and 2 in two separate regions, let us create - three separate regions: A cubic region for the simulation box and two - additional regions for placing the atoms: - -.. code-block:: lammps - - # 2) System definition - region simulation_box block -20 20 -20 20 -20 20 - create_box 2 simulation_box - region region_cylinder_in cylinder z 0 0 10 INF INF side in - region region_cylinder_out cylinder z 0 0 10 INF INF side out - create_atoms 1 random 1000 341341 region_cylinder_out - create_atoms 2 random 150 127569 region_cylinder_in - -.. container:: justify - - The *side in* and *side out* keywords - are used to define regions that are respectively inside - and outside of the cylinder of radius 10. Then, copy similar lines - as previously into *input.min.lammps*: - -.. code-block:: lammps - - # 3) Simulation settings - mass 1 1 - mass 2 1 - pair_coeff 1 1 1.0 1.0 - pair_coeff 2 2 0.5 3.0 - - # 4) Visualization - thermo 10 - thermo_style custom step temp pe ke etotal press - dump mydmp all atom 10 dump.min.lammpstrj - - # 5) Run - minimize 1.0e-4 1.0e-6 1000 10000 - write_data minimized_coordinate.data - -.. container:: justify - - The main novelty, compared to the previous input script, is the *write_data* - command. This command is used to print the final state of the simulation in - a file named *minimized_coordinate.data*. Note that the *write_data* command - is placed after the *minimize* command. This *.data* file will be used later - to restart the simulation from the final state of the energy minimization step. - -.. container:: justify - - Run the *input.min.lammps* script using LAMMPS. - -.. container:: justify - - As soon as the simulation starts, a new dump file named *dump.min.lammpstrj* - must appear in the folder. This *.lammpstrj* can be used to visualize the - atom's trajectories during minimization using VMD. At the end of the simulation, - a file named *minimized_coordinate.data* is created by LAMMPS. - -.. container:: justify - - If you open *minimized_coordinate.data* with a text editor, you can see that - it contains all the information necessary to restart the simulation, such as - the number of atoms, the box size, the *masses*, and the *pair_coeffs*: - -.. code-block:: lammps - - 1150 atoms - 2 atom types - - -20 20 xlo xhi - -20 20 ylo yhi - -20 20 zlo zhi - - Masses - - 1 1 - 2 1 - - Pair Coeffs # lj/cut - - 1 1 1 - 2 0.5 3 - (...) - -.. container:: justify - - The *minimized_coordinate.data* file also contains the final - positions of the atoms: - -.. code-block:: lammps - - (...) - Atoms # atomic - - 970 1 4.4615279184230525 -19.88248310680258 -19.497251754277872 0 0 0 - 798 1 1.0773937287460968 -17.57843015813612 -19.353475858951473 0 0 0 - 21 1 -17.542385434367777 -16.647460269156497 -18.93914807895693 0 0 0 - 108 1 -15.96241088290946 -15.956274144833264 -19.016419910024062 0 0 0 - 351 1 0.08197850837343444 -16.852380573900156 -19.28249747472579 0 0 0 - 402 1 -5.270160783673711 -15.592291204068946 -19.6382667867645 0 0 0 - (...) - -.. container:: justify - - The first five columns of the *Atoms* section correspond (from left to right) - to the atom indexes (from 1 to the total number of atoms, 1150), the atom types (1 or 2 - here), and the atoms positions :math:`x`, :math:`y`, :math:`z`. The last - three columns are image flags that keep track of which atoms crossed the - periodic boundary. - -Restarting from a saved configuration -------------------------------------- - -.. container:: justify - - Let us create a new input file and start a molecular dynamics simulation - directly from the previously saved configuration. Within *improved-input/*, - create a new file named *input.md.lammps* and copy the same lines as previously: - -.. code-block:: lammps - - # 1) Initialization - units lj - dimension 3 - atom_style atomic - pair_style lj/cut 2.5 - boundary p p p - -.. container:: justify - - Here, instead of creating a new region and adding atoms to it, we can simply - import the previously saved configuration by adding the following command - to input.md.lammps: - -.. code-block:: lammps - - # 2) System definition - read_data minimized_coordinate.data - -.. container:: justify - - By visualizing the previously generated *dump.min.lammpstrj* - file, you may have noticed that some atoms have moved from one region to - the other during minimization. To start the simulation from a clean slate, with - only atoms of type 2 within the cylinder and atoms of type 1 outside the - cylinder, let us delete the misplaced atoms by adding the following commands - to *input.md.lammps*: - -.. code-block:: lammps - - read_data minimized_coordinate.data - region region_cylinder_in cylinder z 0 0 10 INF INF side in - region region_cylinder_out cylinder z 0 0 10 INF INF side out - group group_type_1 type 1 - group group_type_2 type 2 - group group_region_in region region_cylinder_in - group group_region_out region region_cylinder_out - group group_type_1_in intersect group_type_1 group_region_in - group group_type_2_out intersect group_type_2 group_region_out - delete_atoms group group_type_1_in - delete_atoms group group_type_2_out - -.. container:: justify - - The two first *region* commands recreate - the previously defined regions, which is necessary since - regions are not saved by the *write_data* command. - -.. container:: justify - - The first two *group* commands are used to create groups containing - all the atoms of type 1 and all the atoms of type 2, respectively. - The next two *group* commands create atom groups based on their - positions at the beginning of the simulation, i.e. when the commands - are being read by LAMMPS. The last two *group* commands create atom groups - based on the intersection between the previously defined groups. - -.. container:: justify - - Finally, the two *delete_atoms* commands delete the - atoms of type 1 that are located within the cylinder and the atoms of - type 2 that are located outside the cylinder, respectively. - -.. container:: justify - - When you run the *input.md.lammps* input using LAMMPS, you - can see in the *log* file how many atoms are in each group, - and how many atoms have been deleted: - -.. code-block:: bw - - 1000 atoms in group group_type_1 - 150 atoms in group group_type_2 - 149 atoms in group group_region_in - 1001 atoms in group group_region_out - 0 atoms in group group_type_1_in - 1 atoms in group group_type_2_out - Deleted 0 atoms, new total = 1150 - Deleted 1 atoms, new total = 1149 - -.. container:: justify - - Add the following lines into *input.md.lammps*. - Note the absence of *Simulation settings* section, - because the settings are taken from the *.data* file. - -.. code-block:: lammps - - # 4) Visualization - thermo 1000 - dump mydmp all atom 1000 dump.md.lammpstrj - -.. container:: justify - - Let us extract the number of atoms of each type - inside the cylinder as a function of time, by - adding the following commands to *input.md.lammps*: - -.. code-block:: lammps - - variable n_type1_in equal count(group_type_1,region_cylinder_in) - variable n_type2_in equal count(group_type_2,region_cylinder_in) - fix myat1 all ave/time 10 200 2000 v_n_type1_in & - file output-population1vstime.dat - fix myat2 all ave/time 10 200 2000 v_n_type2_in & - file output-population2vstime.dat - -.. container:: justify - - The two *variables* are used to count the number of atoms of a specific - group in the *region_cylinder_in* region. - -.. container:: justify - - The two *fix ave/time* are calling the previously defined variables and are - printing their values into text files. By using *10 200 2000*, variables are - evaluated every 10 steps, averaged 200 times, and printed in the *.dat* files - every 2000 steps. - -.. container:: justify - - In addition to counting the atoms in each region, let us also extract the - coordination number per atom between atoms of types 1 and 2. The - coordination number is a measure of the average number of type 2 atoms - in the vicinity of type 1 atoms, serving as a good indicator of - the degree of mixing in a binary mixture. Add the following lines into - *input.md.lammps*: - -.. code-block:: lammps - - compute coor12 group_type_1 coord/atom cutoff 2.0 group group_type_2 - compute sumcoor12 all reduce ave c_coor12 - fix myat3 all ave/time 10 200 2000 & - c_sumcoor12 file coordinationnumber12.dat - -.. container:: justify - - The *compute ave* is used to average the per atom - coordination number that is calculated by the *coord/atom* compute. - This averaging is necessary as *coord/atom* returns an array where each value corresponds - to a certain couple of atoms i-j. Such an array can't be printed by *fix ave/time*. - Finally, let us complete the script by adding the following lines - to *input.md.lammps*: - -.. code-block:: lammps - - # 5) Run - velocity all create 1.0 4928459 mom yes rot yes dist gaussian - fix mynve all nve - fix mylgv all langevin 1.0 1.0 0.1 1530917 zero yes - timestep 0.005 - run 300000 - write_data mixed.data - -.. container:: justify - - There are a few differences from the previous simulation. - First, the *velocity create* command attributes an initial velocity to every - atom. The initial velocity is chosen so that the average initial - temperature is equal to 1 (unitless). The additional - keywords ensure that no linear momentum (*mom yes*) and no angular - momentum (*rot yes*) are given to the system and that the generated - velocities are distributed as a Gaussian. Another improvement - is the *zero yes* keyword in the Langevin thermostat, which - ensures that the total random force applied to the atoms is equal to zero. - -.. container:: justify - - Run *input.md.lammps* using LAMMPS and visualize the trajectory - using VMD. - -.. figure:: ../figures/level1/lennard-jones-fluid/mixing-vmd-light.png - :alt: LAMMPS VMD tutorial molecular dynamics simulation - :class: only-light - -.. figure:: ../figures/level1/lennard-jones-fluid/mixing-vmd-dark.png - :alt: LAMMPS VMD tutorial molecular dynamics simulation - :class: only-dark - -.. container:: figurelegend - - Figure: Evolution of the system during mixing. The three snapshots show - respectively the system at :math:`t=0` (left panel), - :math:`t=75` (middle panel), and :math:`t=1500` (right panel). - -.. container:: justify - - After running *input.md.lammps* using LAMMPS, you can observe the number - of atoms in each region from the generated data files, as - well as the evolution of the coordination number due to mixing: - -.. figure:: ../figures/level1/lennard-jones-fluid/mixing.png - :alt: Result tutorial molecular dynamics simulation: Energy plot over time - :class: only-light - -.. figure:: ../figures/level1/lennard-jones-fluid/mixing-dm.png - :alt: Result tutorial molecular dynamics simulation: Energy plot over time - :class: only-dark - -.. container:: figurelegend - - Figure: Evolution of the number of atoms within the *region_cylinder_in* region - as a function of time (a), and evolution of the coordination number - between atoms of types 1 and 2 (b). - -.. include:: ../../non-tutorials/accessfile.rst - -Going further with exercises -============================ - -.. include:: ../../non-tutorials/link-to-solutions.rst - -Solve Lost atoms error ----------------------- - -.. container:: justify - - For this exercise, the following input script is provided: - -.. code-block:: lammps - - units lj - dimension 3 - atom_style atomic - pair_style lj/cut 2.5 - boundary p p p - - region simulation_box block -20 20 -20 20 -20 20 - create_box 1 simulation_box - create_atoms 1 random 1000 341841 simulation_box - - mass 1 1 - pair_coeff 1 1 1.0 1.0 - - dump mydmp all atom 100 dump.lammpstrj - thermo 100 - thermo_style custom step temp pe ke etotal press - - fix mynve all nve - fix mylgv all langevin 1.0 1.0 0.1 1530917 - timestep 0.005 - - run 10000 - -.. container:: justify - - As it is, this input returns one of the most common - error that you will encounter using LAMMPS: - -.. code-block:: bash - - ERROR: Lost atoms: original 1000 current 984 - -.. container:: justify - - The goal of this exercise is to fix the *Lost atoms* error without - using any other command than the ones already present. You can - only play with the values of the parameters and/or replicate every - command as many times as needed. - -.. admonition:: Note - :class: info - - This script is failing because particles are created randomly in space, some - of them are likely overlapping, and no energy minimization is performed prior - to start the molecular dynamics simulation. - -Create a demixed dense phase ----------------------------- - -.. container:: justify - - Starting from one of the *input* created in this tutorial, fine-tune the - parameters such as particle numbers and interaction to create a simulation - with the following properties: - - - the density in particles must be high, - - both particles of type 1 and 2 must have the same size, - - particles of type 1 and 2 must demix. - -.. figure:: ../figures/level1/lennard-jones-fluid/demixing-light.png - :alt: VMD/LAMMPS exercise molecular dynamics simulation: demixing lennard - jones fluids - :class: only-light - -.. figure:: ../figures/level1/lennard-jones-fluid/demixing-dark.png - :alt: VMD/LAMMPS exercise molecular dynamics simulation: demixing lennard - jones fluids - :class: only-dark - -.. container:: figurelegend - - Figure: Snapshots taken at different times showing the particles of type 1 - and type 2 progressively demixing and forming large demixed areas. - -.. admonition:: Hint - :class: info - - An easy way to create a dense phase is to allow the box dimensions to relax - until the vacuum disappears. You can do that by replacing the *fix nve* with *fix nph*. - -From atoms to molecules ------------------------ - -.. container:: justify - - Add a bond between particles of *type 2* to create dumbbell molecules instead - of single particles. - -.. figure:: ../figures/level1/lennard-jones-fluid/dumbell-dark.png - :alt: Dumbbell Lennard-Jones molecules simulated using LAMMPS - :class: only-dark - -.. figure:: ../figures/level1/lennard-jones-fluid/dumbell-light.png - :alt: Dumbbell Lennard-Jones molecules simulated using LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: Dumbbell molecules made of 2 large spheres mixed with smaller - particles (small spheres). See the corresponding |dumbell_video|. - -.. |dumbell_video| raw:: html - - video - -.. container:: justify - - Similarly to the dumbbell molecules, create a small polymer, - i.e. a long chain of particles linked by bonds and angles. - -.. figure:: ../figures/level1/lennard-jones-fluid/polymer-dark.png - :alt: Polymer Lennard-Jones molecules simulated using LAMMPS - :class: only-dark - -.. figure:: ../figures/level1/lennard-jones-fluid/polymer-light.png - :alt: Polymer Lennard-Jones molecules simulated using LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: A single small polymer molecule made of 9 large spheres mixed with - smaller particles. See the corresponding |polymer_video|. - -.. |polymer_video| raw:: html - - video - -.. admonition:: Hints - :class: info - - .. container:: justify - - Use a *molecule template* to easily insert as many atoms connected - by bonds (i.e. molecules) as you want. A molecule template typically - begins as follows: - - .. code-block:: lammps - - 2 atoms - 1 bonds - - Coords - - 1 0.5 0 0 - 2 -0.5 0 0 - - (...) - - .. container:: justify - - A bond section also needs to be added, see this - |molecule_template_lammps| for details on the formatting of a - molecule template. - -.. |molecule_template_lammps| raw:: html - - page diff --git a/docs/sphinx/source/tutorials/level2/nanosheared-electrolyte.rst b/docs/sphinx/source/tutorials/level2/nanosheared-electrolyte.rst deleted file mode 100644 index 4a0890934..000000000 --- a/docs/sphinx/source/tutorials/level2/nanosheared-electrolyte.rst +++ /dev/null @@ -1,918 +0,0 @@ -.. _sheared-confined-label: - -Nanosheared electrolyte -*********************** - -.. container:: hatnote - - Aqueous NaCl solution sheared by two walls - -.. figure:: ../figures/level2/nanosheared-electrolyte/nanoconfined-electrolyte-dark.png - :height: 250 - :alt: Electrolyte nano-confined in a slit pore - :class: only-dark - :align: right - -.. figure:: ../figures/level2/nanosheared-electrolyte/nanoconfined-electrolyte-light.png - :height: 250 - :alt: Electrolyte nano-confined in a slit pore - :class: only-light - :align: right - -.. container:: justify - - The objective of this tutorial is to simulate an electrolyte nanoconfined - and sheared by two walls. The density and velocity profiles of the fluid in - the direction normal to the walls are extracted to highlight the effect of - confining a fluid on its local properties. - -.. container:: justify - - This tutorial illustrates some key aspects of - combining a fluid and a solid in the same simulation. - A major difference from :ref:`all-atoms-label` is that - here a rigid four-points water model named TIP4P is used :cite:`abascal2005general`. - TIP4P is one of the most common water models due to its high accuracy. - -.. include:: ../../non-tutorials/recommand-lj.rst - -.. include:: ../../non-tutorials/cite.rst - -.. include:: ../../non-tutorials/2Aug2023.rst - -System preparation -================== - -.. container:: justify - - The fluid and walls must first be generated and then equilibrated at a - reasonable temperature and pressure. - -System generation ------------------ - -.. container:: justify - - Create a new folder called *systemcreation/*. - Within *systemcreation/*, open a blank file - called *input.lammps*, and copy the following lines into it: - -.. code-block:: lammps - - boundary p p f - units real - atom_style full - bond_style harmonic - angle_style harmonic - pair_style lj/cut/tip4p/long 1 2 1 1 0.1546 12.0 - kspace_style pppm/tip4p 1.0e-4 - kspace_modify slab 3.0 - -.. container:: justify - - These lines are used to define the most basic parameters, - including the *atom*, *bond*, and *angle* styles, as well as - interaction potential. Here, *lj/cut/tip4p/long* imposes - a Lennard Jones potential with a cut-off at :math:`12\,\text{Å}` - and a long-range Coulomb potential. - -.. container:: justify - - So far, the commands are relatively similar to those in the previous tutorial, - :ref:`all-atoms-label`, - with two major differences: the use of *lj/cut/tip4p/long* - instead of *lj/cut/coul/long*, - and *pppm/tip4p* - instead of *pppm*. These two tip4p-specific commands allow us to model a four-point water - molecule without explicitly defining the fourth massless atom *M*. The value of - :math:`0.1546\,\text{Å}` corresponds to the *O-M* distance and is - imposed by the water model. Here, |TIP4P-2005| is used :cite:`abascal2005general`. - -.. |TIP4P-2005| raw:: html - - TIP4P-2005 - -.. container:: justify - - Another novelty, here, is the use of *kspace_modify slab 3.0* that is combined - with the non-periodic boundaries along the *z* coordinate: *boundary p p f*. - With the *slab* option, the system is treated as periodical along *z*, but with an - empty volume inserted between the periodic images of the slab, and the interactions - along *z* effectively turned off. - -.. admonition:: About lj/cut/tip4p/long pair style - :class: info - - The *lj/cut/tip4p/long* pair style is similar to the conventional - Lennard Jones and Coulomb interactions, except that it is specifically designed - for four-point water model (tip4p). The atoms of the water model - will be type 1 (O) and 2 (H). All the other atoms in the simulation - are treated *normally* with long-range Coulomb interaction. - -.. container:: justify - - Let us create the box by adding the following lines to *input.lammps*: - -.. code-block:: lammps - - lattice fcc 4.04 - region box block -3 3 -3 3 -5 5 - create_box 5 box & - bond/types 1 & - angle/types 1 & - extra/bond/per/atom 2 & - extra/angle/per/atom 1 & - extra/special/per/atom 2 - -.. container:: justify - - The *lattice* command defines the unit - cell. Here, the face-centered cubic (fcc) lattice with a scale factor of - 4.04 has been chosen for the future positioning of the atoms - of the walls. - -.. container:: justify - - The *region* command defines a geometric - region of space. By choosing *xlo=-3* and *xhi=3*, and - because we have previously chosen a lattice with a scale - factor of 4.04, the region box extends from -12.12 Å to 12.12 Å - along the x direction. - -.. container:: justify - - The *create_box* command creates a simulation box with 5 types of atoms: - the oxygen and hydrogen of the water molecules, - the two ions (:math:`\text{Na}^+`, - :math:`\text{Cl}^-`), and the - atom of the walls. The *create_box* command extends over 6 lines thanks to the - :math:`\&` character. The second and third lines are used to - indicate that the simulation contains 1 type of bond and 1 - type of angle (both required by the water molecule). The parameters for - these bond and angle constraints will be given later. The - three last lines are for memory allocation. - -.. container:: justify - - Now, we can add atoms to the system. First, let us create two - sub-regions corresponding respectively to the two solid - walls, and create a larger region from the union of the two - regions. Then, let us create atoms of type 5 (the wall) within the two - regions. Add the following lines to *input.lammps*: - -.. code-block:: lammps - - region rbotwall block -3 3 -3 3 -4 -3 - region rtopwall block -3 3 -3 3 3 4 - region rwall union 2 rbotwall rtopwall - create_atoms 5 region rwall - -.. container:: justify - - Atoms will be placed in the positions of the previously - defined lattice, thus forming fcc solids. - -.. container:: justify - - In order to add the water molecules, first - download the |download_TIP4P2005.txt| - and place it within *systemcreation/*. The template contains all the - necessary information concerning the water molecule, such as - atom positions, bonds, and angles. - -.. |download_TIP4P2005.txt| raw:: html - - molecule template - -.. container:: justify - - Add the following lines to *input.lammps*: - -.. code-block:: lammps - - region rliquid block INF INF INF INF -2 2 - molecule h2omol RigidH2O.txt - create_atoms 0 region rliquid mol h2omol 482793 - -.. container:: justify - - Within the last three lines, a *region* named *rliquid* is created based on the last defined lattice, *fcc 4.04*. - *rliquid* will be used for depositing the water molecules. - -.. container:: justify - - The *molecule* command opens up the molecule template named - *RigidH2O.txt*, and names the associated molecule *h2omol*. - -.. container:: justify - - The new molecules are placed on the *fcc 4.04* lattice - by the *create_atoms* command. The - first parameter is 0, meaning that the atom IDs from the - *RigidH2O.txt* file will be used. - The number *482793* is a seed that is - required by LAMMPS, it can be any positive integer. - -.. container:: justify - - Finally, let us create 30 ions (15 :math:`\text{Na}^+` - and 15 :math:`\text{Cl}^-`) - in between the water molecules, by adding the following commands to *input.lammps*: - -.. code-block:: lammps - - create_atoms 3 random 15 52802 rliquid overlap 0.3 maxtry 500 - create_atoms 4 random 15 90182 rliquid overlap 0.3 maxtry 500 - set type 3 charge 1 - set type 4 charge -1 - -.. container:: justify - - Each *create_atoms* command will add 15 ions at random positions - within the *rliquid* region, ensuring that there is no *overlap* with existing - molecules. Feel free to increase or decrease the salt - concentration by changing the number of desired ions. To keep the system charge neutral, - always insert the same number of - :math:`\text{Na}^+` - and :math:`\text{Cl}^-`, - unless there are other charges in the system. - -.. container:: justify - - The charges of the newly added ions are specified by the two *set* commands. - -.. container:: justify - - Before starting the simulation, we still need to define the parameters of the simulation: the mass - of the 5 atom types (O, H, :math:`\text{Na}^+`, :math:`\text{Cl}^-`, and wall), the - pairwise interaction parameters (here, the parameters for the - Lennard-Jones potential), and the bond and angle parameters. - Copy the following line into *input.lammps*: - -.. code-block:: lammps - - include ../PARM.lammps - include ../GROUP.lammps - -.. container:: justify - - Create a new text file called *PARM.lammps* next to - the *systemcreation/* folder. Copy the following lines - into PARM.lammps: - -.. code-block:: lammps - - mass 1 15.9994 # water - mass 2 1.008 # water - mass 3 28.990 # ion - mass 4 35.453 # ion - mass 5 26.9815 # wall - - pair_coeff 1 1 0.185199 3.1589 # water - pair_coeff 2 2 0.0 1.0 # water - pair_coeff 3 3 0.04690 2.4299 # ion - pair_coeff 4 4 0.1500 4.04470 # ion - pair_coeff 5 5 11.697 2.574 # wall - pair_coeff 1 5 0.4 2.86645 # water-wall - -.. container:: justify - - Each *mass* command assigns a mass in grams/mole to an atom type. Each - *pair_coeff* assigns respectively the depth of the LJ potential - (in Kcal/mole), and the distance (in Ångstrom) at which the - particle-particle potential energy is 0. - -.. admonition:: About the parameters - :class: info - - The parameters for water - correspond to the TIP4P/2005 water model, for which only - the oxygen interacts through Lennard-Jones potential, and the parameters - for :math:`\text{Na}^+` and :math:`\text{Cl}^-` are - from the CHARMM-27 force field :cite:`mackerell2000development`. - -.. container:: justify - - As already seen in previous tutorials and with the important exception of - *pair_coeff 1 5*, only pairwise interactions between atoms of identical - types was assigned. By default, LAMMPS calculates the pair coefficients for - the interactions between atoms of different types (i and j) by using geometrical average: - :math:`\epsilon_{ij} = (\epsilon_{ii} + \epsilon_{jj})/2`, - :math:`\sigma_{ij} = (\sigma_{ii} + \sigma_{jj})/2.`. - If the default value of :math:`5.941\,\text{kcal/mol}` - was kept for :math:`\epsilon_\text{1-5}`, the solid walls would be extremely - hydrophilic, causing the water molecule to form dense layers. As a comparison, - the water-water energy :math:`\epsilon_\text{1-1}` is only - :math:`0.185199\,\text{kcal/mol}`. Therefore, the walls were made less - hydrophilic by reducing the value of :math:`\epsilon_\text{1-5}`. Copy the - following lines into PARM.lammps as well: - -.. code-block:: lammps - - bond_coeff 1 0 0.9572 # water - - angle_coeff 1 0 104.52 # water - -.. container:: justify - - The *bond_coeff* command, used here for the O-H bond of the water molecule, sets both - the spring constant of the harmonic potential and the equilibrium distance - of :math:`0.9572~\text{Å}`. The constant can be 0 for a rigid water molecule, - because the shape of the molecule will be preserved by the SHAKE algorithm - (see below) :cite:`ryckaert1977numerical, andersen1983rattle`. - Similarly, the angle coefficient for the H-O-H angle of the water - molecule sets the force constant of the angular harmonic potential to 0 and - the equilibrium angle to :math:`104.52^\circ`. - -.. container:: justify - - Let us also create another file called *GROUP.lammps* next - to *PARM.lammps*, and copy the following lines into it: - -.. code-block:: lammps - - group H2O type 1 2 - group Na type 3 - group Cl type 4 - group ions union Na Cl - group fluid union H2O ions - - group wall type 5 - region rtop block INF INF INF INF 0 INF - region rbot block INF INF INF INF INF 0 - group top region rtop - group bot region rbot - group walltop intersect wall top - group wallbot intersect wall bot - -.. container:: justify - - As it is now, the fluid density within the two walls is too high. - To avoid high density and pressure, let us add the following lines - to *input.lammps* to delete about :math:`15~\%` - of the water molecules: - -.. code-block:: lammps - - delete_atoms random fraction 0.15 yes H2O NULL 482793 mol yes - -.. container:: justify - - Finally, add the following lines to *input.lammps*: - -.. code-block:: lammps - - run 0 - - write_data system.data nocoeff - write_dump all atom dump.lammpstrj - -.. container:: justify - - With *run 0*, the simulation will run for 0 steps, which is - enough for creating the system and saving the final state. - -.. container:: justify - - The *write_data* creates a file named *system.data* - containing all the information required to restart the - simulation from the final configuration generated by this - input file. With the *nocoeff* option, the parameters from the force field - are not written in the *.data* file. - -.. container:: justify - - The *write_dump* command prints the final - positions of the atoms, and can be opened with VMD - to visualize the system. - -.. container:: justify - - Run the *input.lammps* file using LAMMPS. - -.. figure:: ../figures/level2/nanosheared-electrolyte/systemcreation-light.png - :alt: LAMMPS: electrolyte made of water and salt between walls - :class: only-light - -.. figure:: ../figures/level2/nanosheared-electrolyte/systemcreation-dark.png - :alt: LAMMPS: electrolyte made of water and salt between walls - :class: only-dark - -.. container:: figurelegend - - Figure: Side view of the system. Periodic images are represented in darker - colors. Water molecules are in red and white, :math:`\text{Na}^+` - ions in purple, :math:`\text{Cl}^-` ions in lime, and wall atoms in - gray. Note the absence of atomic defect at the cell boundaries. - See the corresponding |youtube_video_nanosheared|. - -.. |youtube_video_nanosheared| raw:: html - - video - -.. container:: justify - - Always check that your system has been correctly created - by looking at the periodic images. Atomic defects may - occur at the boundary. - -Energy minimization -------------------- - -.. admonition:: Why is energy minimization necessary? - :class: info - - It is clear from the way the system has been created that - the atoms are not at equilibrium distances from each - other. Indeed, some ions added using the *create_atoms* - commands are too close to the water molecules. - If we were to start a *normal* (i.e. with a timestep of about 1 fs) - molecular dynamics simulation now, the atoms - would exert huge forces on each other, accelerate - brutally, and the simulation would likely fail. - -.. admonition:: Dealing with overlapping atoms - :class: info - - MD simulations failing due to overlapping atoms are - extremely common. If it occurs, you can either - - - delete the overlapping atoms using the *delete_atoms* command of LAMMPS, - - move the atoms to more reasonable distances before the simulation starts using energy minimization, or using molecular dynamics with a small timestep. - -.. container:: justify - - Let us move the atoms and place them - in more energetically favorable positions before starting the simulation. - Let us call this step *energy minimization*, although it is not - a conventional *minimization* as done for instance - in tutorial :ref:`lennard-jones-label`. Instead, a molecular dynamics simulation - will be performed here, with some techniques employed to prevent the system - from exploding due to overlapping atoms. - -.. container:: justify - - To perform this energy minimization, let us - create a new folder named *minimization/* next to *systemcreation/*, - and create a new input file named *input.lammps* in it. Copy the following lines - in *input.lammps*: - -.. code-block:: lammps - - boundary p p p - units real - atom_style full - bond_style harmonic - angle_style harmonic - pair_style lj/cut/tip4p/long 1 2 1 1 0.1546 12.0 - kspace_style pppm/tip4p 1.0e-4 - kspace_modify slab 3.0 - - read_data ../systemcreation/system.data - - include ../PARM.lammps - include ../GROUP.lammps - -.. container:: justify - - The only difference from the previous input is that instead - of creating a new box and new atoms, we open the - previously created file *system.data* located in *systemcreation/*. - The file *system.data* contains the definition of the simulation box - and the positions of the atoms. - -.. container:: justify - - Now, let us create a first simulation step using a relatively small - timestep (:math:`0.5\,\text{fs}`) and a low temperature - of :math:`T = 1\,\text{K}`: - -.. code-block:: lammps - - fix mynve fluid nve/limit 0.1 - fix myber fluid temp/berendsen 1 1 100 - fix myshk H2O shake 1.0e-4 200 0 b 1 a 1 - timestep 0.5 - -.. container:: justify - - Just like *fix nve*, the *fix nve/limit* command performs constant NVE integration to - update the positions and velocities of the atoms at each - timestep. The difference is that *fix nve/limit* also limits the maximum - distance atoms can travel at each timestep. The chosen maximum distance in - :math:`0.1~\text{Å}`. Because the *fix nve/limit* is applied to the group *fluid*, - only the water molecules and ions will move. - -.. container:: justify - - The *fix temp/berendsen* rescales the - velocities of the atoms to force the temperature of the system - to reach the desired value of :math:`1~\text{K}`, and the SHAKE algorithm - is used in order to maintain the shape of the water molecules. - -.. container:: justify - - Let us also print the atom positions in a *.lammpstrj* file - and control the printing of thermodynamic outputs by - adding the following lines to *input.lammps*: - -.. code-block:: lammps - - dump mydmp all atom 1000 dump.lammpstrj - thermo 200 - -.. container:: justify - - Finally, let us run for 4000 steps. Add the - following lines to *input.lammps*: - -.. code-block:: lammps - - run 4000 - -.. container:: justify - - In order to better equilibrate the system, let us perform - two additional steps with a larger timestep and a higher - imposed temperature: - -.. code-block:: lammps - - fix myber fluid temp/berendsen 300 300 100 - timestep 1.0 - - run 4000 - - unfix mynve - fix mynve fluid nve - - run 4000 - - write_data system.data nocoeff - -.. container:: justify - - For the last of the three steps, fix *nve* is used instead of - *nve/limit*, which will allow for a better relaxation of the - atom positions. - -.. container:: justify - - When running the *input.lammps* file with LAMMPS, you should see that - the total energy of the system decreases during the first - of the 3 steps, before re-increasing a little after the - temperature is increased from 1 to :math:`300\,\text{K}`. - -.. figure:: ../figures/level2/nanosheared-electrolyte/minimization.png - :alt: Energy minimisation of the confined water and salt - :class: only-light - -.. figure:: ../figures/level2/nanosheared-electrolyte/minimization-dm.png - :alt: Energy minimisation of the confined water and salt - :class: only-dark - -.. container:: figurelegend - - Figure: Total energy of the system :math:`E_\text{tot}` as a function of - time :math:`t` extracted from the log - file using *Python* and *lammps_logfile*. The vertical dashed lines demarcate the three consecutive steps. - -.. container:: justify - - If you look at the trajectory using VMD, you will see that some of the atoms - are moving, particularly those that were initially in problematic positions. - -System equilibration --------------------- - -.. container:: justify - - Let us equilibrate further the entire system by letting both - fluid and piston relax at ambient temperature. - -.. container:: justify - - Create a new folder called *equilibration/* next to - the previously created folders, and create a new - *input.lammps* file in it. Add the following lines to *input.lammps*: - -.. code-block:: lammps - - boundary p p f - units real - atom_style full - bond_style harmonic - angle_style harmonic - pair_style lj/cut/tip4p/long 1 2 1 1 0.1546 12.0 - kspace_style pppm/tip4p 1.0e-4 - kspace_modify slab 3.0 - - read_data ../minimization/system.data - - include ../PARM.lammps - include ../GROUP.lammps - - fix mynve all nve - fix myber all temp/berendsen 300 300 100 - fix myshk H2O shake 1.0e-4 200 0 b 1 a 1 - fix myrct all recenter NULL NULL 0 - timestep 1.0 - -.. container:: justify - - The fix *recenter* has no influence on the dynamics, but will - keep the system in the center of the box, which makes the - visualization easier. - -.. container:: justify - - Then, add the following lines to *input.lammps* for - the trajectory visualization and output: - -.. code-block:: lammps - - dump mydmp all atom 1000 dump.lammpstrj - thermo 500 - variable walltopz equal xcm(walltop,z) - variable wallbotz equal xcm(wallbot,z) - variable deltaz equal v_walltopz-v_wallbotz - fix myat1 all ave/time 100 1 100 v_deltaz file interwall_distance.dat - -.. container:: justify - - The first two variables extract the centers of mass of - the two walls. Then, the *deltaz* - variable is used to calculate the distance between - the two variables *walltopz* - and *wallbotz*, i.e. the distance between the two walls. - -.. container:: justify - - Finally, let us add the *run* command: - -.. code-block:: lammps - - run 30000 - write_data system.data nocoeff - -.. container:: justify - - Run the *input.lammps* file using LAMMPS. - -.. container:: justify - - As seen from the data printed by *fix myat1*, - the distance between the two walls - reduces until it reaches an equilibrium value. - -.. figure:: ../figures/level2/nanosheared-electrolyte/equilibration.png - :alt: Plot showing the distance between the walls as a function of time. - :class: only-light - -.. figure:: ../figures/level2/nanosheared-electrolyte/equilibration-dm.png - :alt: Plot showing the distance between the walls as a function of time. - :class: only-dark - -.. container:: figurelegend - - Figure: Distance between the walls as a function of time. - After a few picoseconds, the distance between the two walls equilibrates near - its final value. - -.. container:: justify - - Note that it is generally recommended to run longer equilibration. - Here, for instance, the slowest - process in the system is probably the ionic diffusion. Therefore, the equilibration - should in principle be longer than the time - the ions need to diffuse over the size of the pore - (:math:`\approx 1.2\,\text{nm}`), i.e. on the order of half a nanosecond. - -Imposed shearing -================ - -.. container:: justify - - From the equilibrated configuration, let us impose a lateral - motion to the two walls and shear the electrolyte. - In a new folder called *shearing/*, - create a new *input.lammps* file that starts like the previous ones: - -.. code-block:: lammps - - boundary p p f - units real - atom_style full - bond_style harmonic - angle_style harmonic - pair_style lj/cut/tip4p/long 1 2 1 1 0.1546 12.0 - kspace_style pppm/tip4p 1.0e-4 - kspace_modify slab 3.0 - -.. container:: justify - - Let us import the previously equilibrated data, - include the parameter and group files, - and then deal with the dynamics of the system. - -.. code-block:: lammps - - read_data ../equilibration/system.data - - include ../PARM.lammps - include ../GROUP.lammps - - fix mynve all nve - compute Tfluid fluid temp/partial 0 1 1 - fix myber1 fluid temp/berendsen 300 300 100 - fix_modify myber1 temp Tfluid - compute Twall wall temp/partial 0 1 1 - fix myber2 wall temp/berendsen 300 300 100 - fix_modify myber2 temp Twall - fix myshk H2O shake 1.0e-4 200 0 b 1 a 1 - fix myrct all recenter NULL NULL 0 - -.. container:: justify - - One difference with the previous input is that, here, two thermostats are used, - one for the fluid (*myber1*) and one - for the solid (*myber2*). The use of *fix_modify* together - with *compute temp* ensures that the right temperature value - is used by the thermostats. - -.. container:: justify - - The use of temperature *compute* with *temp/partial 0 1 1* - is meant to exclude the *x* coordinate from the - thermalization, which is important since a large velocity - will be imposed along *x*. - -.. container:: justify - - Then, let us impose the velocity of the two walls - by adding the following commands to *input.lammps*: - -.. code-block:: lammps - - fix mysf1 walltop setforce 0 NULL NULL - fix mysf2 wallbot setforce 0 NULL NULL - velocity wallbot set -2e-4 NULL NULL - velocity walltop set 2e-4 NULL NULL - -.. container:: justify - - The *setforce* commands cancel the forces on *walltop* and - *wallbot*, respectively. Therefore the atoms of the two groups do not - experience any force from the rest of the system. In the absence of force - acting on those atoms, they will conserve their initial velocity. - -.. container:: justify - - The *velocity* commands act only once and impose - the velocity of the atoms of the groups *wallbot* - and *walltop*, respectively. - -.. container:: justify - - Finally, let us dump the atom positions, extract the - velocity profiles using several *ave/chunk* commands, extract the - force applied on the walls, and then run for :math:`200\,\text{ps}` - Add the following lines to *input.lammps*: - -.. code-block:: lammps - - dump mydmp all atom 5000 dump.lammpstrj - thermo 500 - thermo_modify temp Tfluid - - compute cc1 H2O chunk/atom bin/1d z 0.0 1.0 - compute cc2 wall chunk/atom bin/1d z 0.0 1.0 - compute cc3 ions chunk/atom bin/1d z 0.0 1.0 - - fix myac1 H2O ave/chunk 10 15000 200000 & - cc1 density/mass vx file water.profile_1A.dat - fix myac2 wall ave/chunk 10 15000 200000 & - cc2 density/mass vx file wall.profile_1A.dat - fix myac3 ions ave/chunk 10 15000 200000 & - cc3 density/mass vx file ions.profile_1A.dat - - fix myat1 all ave/time 10 100 1000 f_mysf1[1] f_mysf2[1] file forces.dat - - timestep 1.0 - run 200000 - write_data system.data nocoeff - -.. container:: justify - - Here, a binning of :math:`1\,\text{Å}` is used for the density profiles - generated by the *ave/chunk* commands. For smoother profiles, you can - reduce its value. - -.. container:: justify - - The averaged velocity profile of the fluid - can be plotted. As expected for such Couette flow geometry, the velocity - of the fluid is found to increase linearly along :math:`z`. - -.. figure:: ../figures/level2/nanosheared-electrolyte/shearing.png - :alt: Velocity of the nanosheared fluid - :class: only-light - -.. figure:: ../figures/level2/nanosheared-electrolyte/shearing-dm.png - :alt: Velocity of the nanosheared fluid - :class: only-dark - -.. container:: figurelegend - - Figure: Velocity profiles for water molecules, ions and walls - along the *z* axis. The line is a linear fit assuming that - the pore size is :math:`h = 1.8\,\text{nm}`. - -.. figure:: ../figures/level2/nanosheared-electrolyte/density.png - :alt: density of the nanosheared fluid - :class: only-light - -.. figure:: ../figures/level2/nanosheared-electrolyte/density-dm.png - :alt: density of the nanosheared fluid - :class: only-dark - -.. container:: figurelegend - - Figure: Water density :math:`\rho` profile - along the *z* axis. - -.. container:: justify - - From the force applied by the fluid on the solid, one can - extract the stress within the fluid, which allows for the measurement of - its viscosity :math:`\dot{\eta}` - according to |reference_gravelle2021|: - :math:`\eta = \tau / \dot{\gamma}` where :math:`\tau` - is the stress applied by the fluid on the shearing wall, and - :math:`\dot{\gamma}` the shear rate (which is imposed - here) :cite:`gravelle2021violations`. Here, the shear rate - is approximatively :math:`\dot{\gamma} = 16 \cdot 10^9\,\text{s}^{-1}`, - and using a surface area of :math:`A = 6 \cdot 10^{-18}\,\text{m}^2`, one - gets an estimate for the shear viscosity for the confined - fluid of :math:`\eta = 6.6\,\text{mPa.s}`. - -.. |reference_gravelle2021| raw:: html - - gravelle2021 - -.. container:: justify - - The viscosity calculated at such a high shear rate may - differ from the expected *bulk* value. In general, it is recommended to use a lower - value for the shear rate. Note that for lower shear rates, the ratio of noise-to-signal - is larger, and longer simulations are needed. - -.. container:: justify - - Another important point to keep in mind is that the viscosity of a fluid - next to a solid surface is typically larger than in bulk due to interaction with the - walls :cite:`wolde-kidanInterplayInterfacialViscosity2021`. Therefore, one expects the present simulation to return - a viscosity that is slightly larger than what would - be measured in the absence of a wall. - -.. include:: ../../non-tutorials/accessfile.rst - -Going further with exercises -============================ - -.. include:: ../../non-tutorials/link-to-solutions.rst - -Induce a Poiseuille flow ------------------------- - -.. container:: justify - - Instead of inducing a shearing of the fluid using the walls, - induce a net flux of the liquid in the direction tangential - to the walls. The walls must be kept immobile. - -.. container:: justify - - Extract the velocity profile, and make sure that the - resulting velocity profile is consistent with the Poiseuille equation, - which can be derived from the Stokes equation :math:`\eta \nabla \textbf{v} = - \textbf{f} \rho` - where :math:`f` is the applied force, - :math:`\rho` is the fluid density, - :math:`\eta` is the fluid viscosity. - -.. figure:: ../figures/level2/nanosheared-electrolyte/shearing-poiseuille-light.png - :alt: Velocity of the fluid forming a Poiseuille flow - :class: only-light - -.. figure:: ../figures/level2/nanosheared-electrolyte/shearing-poiseuille-dark.png - :alt: Velocity of the fluid forming a Poiseuille flow - :class: only-dark - -.. container:: figurelegend - - Figure: Velocity profiles of the water molecules along the *z* axis (disks). - The line is the Poiseuille equation. - -.. container:: justify - - An important step is to choose the proper value for the additional force. \ No newline at end of file diff --git a/docs/sphinx/source/tutorials/level2/polymer-in-water.rst b/docs/sphinx/source/tutorials/level2/polymer-in-water.rst deleted file mode 100644 index ee1e5b465..000000000 --- a/docs/sphinx/source/tutorials/level2/polymer-in-water.rst +++ /dev/null @@ -1,858 +0,0 @@ -.. _all-atoms-label: - -Polymer in water -**************** - -.. container:: hatnote - - Solvating and stretching a small polymer molecule - -.. figure:: ../figures/level2/polymer-in-water/PEG-dark.webp - :alt: Movie of a peg polymer molecule in water as simulated with LAMMPS - :height: 250 - :align: right - :class: only-dark - -.. figure:: ../figures/level2/polymer-in-water/PEG-light.webp - :alt: Movie of a peg polymer molecule in water as simulated with LAMMPS - :height: 250 - :align: right - :class: only-light - -.. container:: justify - - The goal of this tutorial is to use LAMMPS to solvate a small - hydrophilic polymer (PEG - PolyEthylene Glycol) in a reservoir of water. - -.. container:: justify - - Once the water reservoir is properly equilibrated at the desired temperature - and pressure, the polymer molecule is added and a constant stretching force - is applied to both ends of the polymer. The evolution of the polymer length - is measured as a function of time. The GROMOS 54A7 force field - :cite:`schmid2011definition` is used for the PEG, the SPC/Fw - model :cite:`wu2006flexible` is used for the water, and the long-range - Coulomb interactions are solved using the PPPM solver :cite:`luty1996calculating`. - -.. container:: justify - - This tutorial was inspired by a |Liese2017| by Liese and coworkers, in which - molecular dynamics simulations are - compared with force spectroscopy experiments :cite:`liese2017hydration`. - -.. |Liese2017| raw:: html - - publication - -.. include:: ../../non-tutorials/recommand-lj.rst - -.. include:: ../../non-tutorials/cite.rst - -.. include:: ../../non-tutorials/2Aug2023.rst - -Preparing the water reservoir -============================= - -.. container:: justify - - In this tutorial, the water reservoir is first prepared in the absence of - the polymer. A rectangular box of water is created and - equilibrated at ambient temperature and ambient pressure. - The SPC/Fw water model is used :cite:`wu2006flexible`, which is - a flexible variant of the rigid SPC (simple point charge) - model :cite:`berendsen1981interaction`. - -.. container:: justify - - Create a folder named *pureH2O/*. Inside this folder, create - an empty text file named *input.lammps*. Copy the following - lines into it: - -.. code-block:: lammps - - units real - atom_style full - bond_style harmonic - angle_style harmonic - dihedral_style harmonic - pair_style lj/cut/coul/long 10 - kspace_style pppm 1e-5 - special_bonds lj 0.0 0.0 0.5 coul 0.0 0.0 1.0 angle yes - -.. container:: justify - - With the unit style *real*, masses are in grams per - mole, distances in Ångstroms, time in femtoseconds, and energies - in Kcal/mole. With the *atom_style full*, each atom is a dot - with a mass and a charge that can be - linked by bonds, angles, dihedrals, and/or impropers. The *bond_style*, - *angle_style*, and *dihedral_style* commands define the - potentials for the bonds, angles, and dihedrals used in the simulation, - here *harmonic*. - -.. container:: justify - - Always refer to the LAMMPS |lammps_documentation| if you have doubts about the - potential used by LAMMPS. For instance, this |lammps_documentation_angle_harmonic| - gives the expression for the harmonic angular potential. - -.. |lammps_documentation| raw:: html - - documentation - -.. |lammps_documentation_angle_harmonic| raw:: html - - page - -.. container:: justify - - Finally, the *special_bonds* command, which was already seen in - the previous tutorial, :ref:`carbon-nanotube-label`, sets the LJ and Coulomb - weighting factors for the interaction between neighboring atoms. - -.. admonition:: About *special bonds* - :class: info - - Usually, molecular dynamics force fields are parametrized assuming that - the first neighbors within a molecule do not - interact directly through LJ or Coulomb potential. Here, since we - use *lj 0.0 0.0 0.5* and *coul 0.0 0.0 1.0*, the first and second - neighbors in a molecule only interact through direct bond interactions. - For the third neighbor (here third neighbor only concerns the PEG molecule, - not the water), only half of the LJ interaction will be taken into account, - and the full Coulomb interaction will be used. - -.. container:: justify - - With the *pair_style* named *lj/cut/coul/long*, atoms - interact through both a Lennard-Jones (LJ) potential and - Coulomb interactions. The value of :math:`10\,\text{Å}` is - the cutoff. - -.. admonition:: About cutoff in molecular dynamics - :class: info - - The cutoff of :math:`10\,\text{Å}` applies to both LJ and Coulomb - interactions, but in a different way. For LJ *cut* - interactions, atoms interact with each other only if they - are separated by a distance smaller than the cutoff. For - Coulomb *long*, interactions between atoms closer than - the cutoff are computed directly, and interactions between - atoms outside that cutoff are computed in the reciprocal space. - -.. container:: justify - - Finally, the *kspace* command defines the long-range solver for the - Coulomb interactions. The *pppm* style refers to - particle-particle particle-mesh :cite:`luty1996calculating`. - -.. admonition:: About PPPM - :class: info - - Extracted from |Luty and van Gunsteren|: - The PPPM method is based on separating the total interaction - between particles into the sum of short-range - interactions, which are computed by direct - particle-particle summation, and long-range interactions, - which are calculated by solving Poisson's equation using - periodic boundary conditions (PBCs) :cite:`luty1996calculating`. - -.. |Luty and van Gunsteren| raw:: html - - Luty and van Gunsteren - -.. container:: justify - - Then, let us create a 3D simulation box of dimensions :math:`9 \times 3 \times 3 \; \text{nm}^3`, - and make space for 9 atom types (2 for - the water + 7 for the polymer), 7 bond types (1 for - the water + 6 for the polymer), 8 - angle types (1 for the water + 7 for the polymer), and 4 dihedral types - (for the polymer only). - Copy the following lines into *input.lammps*: - -.. code-block:: lammps - - region box block -45 45 -15 15 -15 15 - create_box 9 box & - bond/types 7 & - angle/types 8 & - dihedral/types 4 & - extra/bond/per/atom 3 & - extra/angle/per/atom 6 & - extra/dihedral/per/atom 10 & - extra/special/per/atom 14 - -.. admonition:: About extra per atom commands - :class: info - - The *extra/x/per/atom* commands are here for - memory allocation. These commands ensure that enough memory space is left for a - certain number of attributes for each atom. We won't worry - about those commands in this tutorial, just keep that in mind if one day - you see the following error - message *ERROR: Molecule topology/atom exceeds system topology/atom*. - -.. container:: justify - - Let us create a *PARM.lammps* file containing all the - parameters (masses, interaction energies, bond equilibrium - distances, etc). In *input.lammps*, add the following line: - -.. code-block:: lammps - - include ../PARM.lammps - -.. container:: justify - - Then, download and save the |PARM_PEG.data| file - next to the *pureH2O/* folder. - -.. |PARM_PEG.data| raw:: html - - parameter - -.. container:: justify - - Within *PARM.lammps*, the *mass* and *pair_coeff* of atoms - of types 8 and 9 are for water and the - atoms of types 1 to 7 are for the polymer - molecule. Similarly, the *bond_coeff 7* and - *angle_coeff 8* are for water, while all - the other parameters are for the polymer. - -.. container:: justify - - Let us create water molecules. To do so, let us - import a molecule template called - *H2O-SPCFw.mol* and then let us randomly create 1050 molecules. - Add the following lines into *input.lammps*: - -.. code-block:: lammps - - molecule h2omol H2O-SPCFw.mol - create_atoms 0 random 1050 87910 NULL mol & - h2omol 454756 overlap 1.0 maxtry 50 - -.. container:: justify - - The *overlap 1* option of the *create_atoms* command ensures that no atoms are - placed exactly in the same position, as this would cause the simulation to - crash. The *maxtry 50* asks LAMMPS to try at most - 50 times to insert the molecules, which is useful in case some - insertion attempts are rejected due to overlap. In some cases, depending on - the system and the values of *overlap* - and *maxtry*, LAMMPS may not create the desired number of molecules. - Always check the number of created atoms in the *log* file after - starting the simulation: - -.. code-block:: bw - - Created 3150 atoms - -.. container:: justify - - When LAMMPS fails to create the desired number of molecules, a WARNING - appears in the *log* file. - -.. container:: justify - - The molecule template named *H2O-SPCFw.mol* - can be |download_FlexibleH2O| - and saved in the *pureH2O/* folder. - This template contains the necessary structural - information of a water molecule, such as the number of atoms, or the IDs - of the atoms that are connected by bonds, angles, etc. - -.. |download_FlexibleH2O| raw:: html - - downloaded - -.. container:: justify - - Then, let us organize the atoms of types 8 and 9 of the water molecules - in a group named *H2O* and perform a small energy minimization. The - energy minimization is mandatory here given the small *overlap* value - of 1 Ångstrom chosen in the *create_atoms* command. Add the following lines - to *input.lammps*: - -.. code-block:: lammps - - group H2O type 8 9 - minimize 1.0e-4 1.0e-6 100 1000 - reset_timestep 0 - -.. container:: justify - - In general, resetting the step of the simulation to 0 using the - *reset_timestep* command is optional. It is used here because the number - of iterations performed by the *minimize* command is usually not a round - number (since the minimization stops when one of four criteria is reached). - -.. container:: justify - - Let us use the *fix npt* to - control the temperature of the molecules with a Nosé-Hoover thermostat and - the pressure of the system with a Nosé-Hoover barostat - :cite:`nose1984unified, hoover1985canonical, martyna1994constant`, - by adding the following line into *input.lammps*: - -.. code-block:: lammps - - fix mynpt all npt temp 300 300 100 iso 1 1 1000 - -.. container:: justify - - The *fix npt* allows us to impose both a temperature of :math:`300\,\text{K}` - (with a damping constant of :math:`100\,\text{fs}`), - and a pressure of 1 atmosphere (with a damping constant of :math:`1000\,\text{fs}`). - With the *iso* keyword, the three dimensions of the box will be re-scaled - simultaneously. - -.. container:: justify - - Let us print the atom positions in a *.lammpstrj* file every 1000 - steps (i.e. 1 ps), print the temperature volume, and - density every 100 steps in 3 separate data files, and - print the information in the terminal every 1000 steps: - -.. code-block:: lammps - - dump mydmp all atom 1000 dump.lammpstrj - variable mytemp equal temp - variable myvol equal vol - fix myat1 all ave/time 10 10 100 v_mytemp file temperature.dat - fix myat2 all ave/time 10 10 100 v_myvol file volume.dat - variable myoxy equal count(H2O)/3 - variable mydensity equal ${myoxy}/v_myvol - fix myat3 all ave/time 10 10 100 v_mydensity file density.dat - thermo 1000 - -.. container:: justify - - The variable *myoxy* corresponds to the number of atoms - divided by 3, i.e. the number of molecules. - -.. admonition:: On calling variables in LAMMPS - :class: info - - Both dollar sign and underscore can be used to call a previously defined - variable. With the dollar sign, the initial value of the variable is returned, - while with the underscore, the instantaneous value of the variable is returned. - To probe the temporal evolution of a variable with time, - the underscore must be used. - -.. container:: justify - - Finally, let us set the timestep to 1.0 fs, - and run the simulation for 20 ps by adding the - following lines into *input.lammps*: - -.. code-block:: lammps - - timestep 1.0 - run 20000 - - write_data H2O.data - -.. container:: justify - - The final state is written into *H2O.data*. - -.. container:: justify - - If you open the *dump.lammpstrj* file using VMD, you should - see the system quickly reaching its equilibrium volume and density. - -.. figure:: ../figures/level2/polymer-in-water/water-light.png - :alt: Curves showing the equilibration of the water reservoir - :class: only-light - -.. figure:: ../figures/level2/polymer-in-water/water-dark.png - :alt: Curves showing the equilibration of the water reservoir - :class: only-dark - -.. container:: figurelegend - - Figure: Water reservoir after equilibration. Oxygen atoms are in red, and - hydrogen atoms are in white. - -.. container:: justify - - Open the *density.dat* file to ensure that the system converged - toward a (reasonably) well-equilibrated liquid water system during the 20 ps of simulation. - -.. figure:: ../figures/level2/polymer-in-water/density_H2O.png - :alt: Curves showing the equilibration of the water reservoir - :class: only-light - -.. figure:: ../figures/level2/polymer-in-water/density_H2O-dm.png - :alt: Curves showing the equilibration of the water reservoir - :class: only-dark - -.. container:: figurelegend - - Figure: Evolution of the density of water with time. The - density :math:`\rho` reaches - a plateau after :math:`\approx 10\,\text{ps}`. - -.. admonition:: Insufficient simulation duration - :class: info - - A duration of :math:`20~\text{ps}` is not sufficient to reach the actual equilibrium density. - Increase this duration to at least :math:`500~\text{ps}` to obtain a density value that - is comparable with the values given in Ref. :cite:`wu2006flexible`. - -.. container:: justify - - If needed, you can |download_H2O.data| the water reservoir I have - equilibrated and use it to continue with the tutorial. - -.. |download_H2O.data| raw:: html - - download - -Solvating the PEG in water -========================== - -.. container:: justify - - Now that the water reservoir is equilibrated, we can safely - include the PEG polymer in the water. - -.. container:: justify - - The PEG molecule topology was downloaded from the |atb_repo| - repository :cite:`malde2011automated, oostenbrink2004biomolecular`. - It has a formula :math:`\text{C}_{28}\text{H}_{58}\text{O}_{15}`, - and the parameters are taken from - the GROMOS 54A7 force field :cite:`schmid2011definition`. - -.. |atb_repo| raw:: html - - ATB - -.. figure:: ../figures/level2/polymer-in-water/singlePEG-light.png - :alt: PEG in vacuum as simulated with LAMMPS - :class: only-light - -.. figure:: ../figures/level2/polymer-in-water/singlePEG-dark.png - :alt: PEG in vacuum as simulated with LAMMPS - :class: only-dark - -.. container:: figurelegend - - Figure: The PEG molecule in vacuum. The carbon atoms are in gray, - the oxygen atoms in red, and the hydrogen atoms in white. - -.. container:: justify - - Create a second folder alongside *pureH2O/* - and call it *mergePEGH2O/*. Create a new blank file in it, - call it *input.lammps*. Within *input.lammps*, copy the same first lines as - previously: - -.. code-block:: lammps - - units real - atom_style full - bond_style harmonic - angle_style harmonic - dihedral_style harmonic - pair_style lj/cut/coul/long 10 - kspace_style pppm 1e-5 - special_bonds lj 0.0 0.0 0.5 coul 0.0 0.0 1.0 angle yes dihedral yes - -.. container:: justify - - Then, import the previously generated data file *H2O.data* - as well as the *PARM.lammps* file: - -.. code-block:: lammps - - read_data ../pureH2O/H2O.data & - extra/bond/per/atom 3 & - extra/angle/per/atom 6 & - extra/dihedral/per/atom 10 & - extra/special/per/atom 14 - include ../PARM.lammps - -.. container:: justify - - Download the molecule |download_PEG| for the PEG molecule, and then - create a single molecule in the middle of the box: - -.. |download_PEG| raw:: html - - template - -.. code-block:: lammps - - molecule pegmol PEG-GROMOS.mol - create_atoms 0 single 0 0 0 mol pegmol 454756 - -.. container:: justify - - Let us create 2 groups to differentiate the PEG from the H2O, - by adding the following lines into *input.lammps*: - -.. code-block:: lammps - - group H2O type 8 9 - group PEG type 1 2 3 4 5 6 7 - -.. container:: justify - - Water molecules that are overlapping with the PEG must be deleted to avoid - future crashing. Add the following line into *input.lammps*: - -.. code-block:: lammps - - delete_atoms overlap 2.0 H2O PEG mol yes - -.. container:: justify - - Here, the value of 2 Ångstroms for the overlap cutoff was fixed arbitrarily - and can be chosen through trial and error. If the cutoff is too small, the - simulation will crash. If the cutoff is too large, too many water molecules - will unnecessarily be deleted. - -.. container:: justify - - Finally, let us use the *fix npt* to control the temperature, as well as - the pressure by allowing the box size to be rescaled along the *x* axis: - -.. code-block:: lammps - - fix mynpt all npt temp 300 300 100 x 1 1 1000 - timestep 1.0 - -.. container:: justify - - Once more, let us dump the atom positions as well as the system temperature - and volume: - -.. code-block:: lammps - - dump mydmp all atom 100 dump.lammpstrj - thermo 100 - variable mytemp equal temp - variable myvol equal vol - fix myat1 all ave/time 10 10 100 v_mytemp file temperature.dat - fix myat2 all ave/time 10 10 100 v_myvol file volume.dat - -.. container:: justify - - Let us also print the total enthalpy: - -.. code-block:: lammps - - variable myenthalpy equal enthalpy - fix myat3 all ave/time 10 10 100 v_myenthalpy file enthalpy.dat - -.. container:: justify - - Finally, let us perform a short equilibration and print the - final state in a data file. Add the following lines into the data file: - -.. code-block:: lammps - - run 30000 - write_data mix.data - -.. container:: justify - - If you open the *dump.lammpstrj* file using VMD - or have a look at the evolution of the volume in *volume.dat*, - you should see that the box dimensions slightly evolve along *x* - to accommodate the new configuration. In addition, the temperature remains - close to the target value of :math:`300~\text{K}` throughout the entire simulation, - and the enthalpy is almost constant, suggesting that the system was close - to equilibrium from the start. - -.. figure:: ../figures/level2/polymer-in-water/solvatedPEG_light.png - :alt: PEG in water - :class: only-light - -.. figure:: ../figures/level2/polymer-in-water/solvatedPEG_dark.png - :alt: PEG in water - :class: only-dark - -.. container:: figurelegend - - Figure: A single PEG molecule in water. Water molecules are represented as - a transparent continuum field for clarity. - -Stretching the PEG molecule -=========================== - -.. container:: justify - - Here, a constant forcing is applied to the two ends of the PEG molecule - until it stretches. Create a new folder next to the previously created - folders, call it *pullonPEG/*, and create a new input file in it - called *input.lammps*. - -.. container:: justify - - First, let us create a variable *f0* corresponding to the magnitude - of the force we are going to apply: - -.. code-block:: lammps - - variable f0 equal 5 - -.. container:: justify - - The force magnitude of :math:`1\,\text{kcal/mol/Å}` corresponds - to :math:`67.2\,\text{pN}` and was chosen to be large enough to overcome - the thermal agitation and the entropic contribution from both water and PEG - molecules (it was chosen by trial and error). Then, copy the same lines as previously: - -.. code-block:: lammps - - units real - atom_style full - bond_style harmonic - angle_style harmonic - dihedral_style harmonic - pair_style lj/cut/coul/long 10 - kspace_style pppm 1e-5 - special_bonds lj 0.0 0.0 0.5 coul 0.0 0.0 1.0 angle yes dihedral yes - -.. container:: justify - - Start the simulation from the equilibrated PEG-water system and include - again the parameter file by adding the following lines into the *input.lammps*: - -.. code-block:: lammps - - read_data ../mergePEGH2O/mix.data - include ../PARM.lammps - -.. container:: justify - - Then, let us create 4 atom groups: H2O and PEG (as previously), as well - as 2 groups containing only the 2 oxygen atoms of types 6 and 7, - respectively. Atoms of types 6 and 7 correspond to the oxygen atoms - located at the ends of the PEG molecule, which we are going to use to pull - on the PEG molecule. Add the following lines into the *input.lammps*: - -.. code-block:: lammps - - group H2O type 8 9 - group PEG type 1 2 3 4 5 6 7 - group topull1 type 6 - group topull2 type 7 - -.. container:: justify - - Add the following *dump* command to the input to print the atom positions - every 1000 steps: - -.. code-block:: lammps - - dump mydmp all atom 1000 dump.lammpstrj - -.. container:: justify - - Let us use a single Nosé-Hoover thermostat applied to all the atoms by - adding the following lines into *input.lammps*: - -.. code-block:: lammps - - timestep 1.0 - fix mynvt all nvt temp 300 300 100 - -.. container:: justify - - Let us also print the end-to-end distance of the PEG, - here defined as the distance between the groups *topull1* - and *topull2*, as well as the temperature of the system and the gyration - radius of the molecule :cite:`fixmanRadiusGyrationPolymer1962a` - by adding the following lines into *input.lammps*: - -.. code-block:: lammps - - variable mytemp equal temp - fix myat1 all ave/time 10 10 100 v_mytemp file output-temperature.dat - variable x1 equal xcm(topull1,x) - variable x2 equal xcm(topull2,x) - variable y1 equal xcm(topull1,y) - variable y2 equal xcm(topull2,y) - variable z1 equal xcm(topull1,z) - variable z2 equal xcm(topull2,z) - variable delta_r equal sqrt((v_x1-v_x2)^2+(v_y1-v_y2)^2+(v_z1-v_z2)^2) - fix myat2 all ave/time 10 10 100 v_delta_r & - file output-end-to-end-distance.dat - compute rgyr PEG gyration - fix myat3 all ave/time 10 10 100 c_rgyr file gyration-radius.dat - thermo 1000 - -.. container:: justify - - Finally, let us simulate 30 picoseconds without any external forcing: - -.. code-block:: lammps - - run 30000 - -.. container:: justify - - This first run will serve as a benchmark to later quantify the changes - induced by the forcing. Then, let us apply a forcing on the 2 oxygen - atoms using two *add_force* commands, and run for an extra 30 ps: - -.. code-block:: lammps - - fix myaf1 topull1 addforce ${f0} 0 0 - fix myaf2 topull2 addforce -${f0} 0 0 - run 30000 - -.. container:: justify - - Run the *input.lammps* file using LAMMPS. If you open the *dump.lammpstrj* - file using *VMD*, you should see that the PEG molecule eventually aligns - in the direction of the force. - -.. figure:: ../figures/level2/polymer-in-water/pulled_peg_dark.png - :alt: PEG molecule in water - :class: only-dark - -.. figure:: ../figures/level2/polymer-in-water/pulled_peg_light.png - :alt: PEG molecule in water - :class: only-light - -.. container:: figurelegend - - Figure: PEG molecule stretched along the *x* direction in water. - Water molecules are represented as a transparent continuum - field for clarity. See the corresponding |pulled_on_peg|. - -.. |pulled_on_peg| raw:: html - - video - -.. container:: justify - - The evolution of the end-to-end distance over time - shows the PEG adjusting to the external forcing: - -.. figure:: ../figures/level2/polymer-in-water/distance-dm.png - :alt: plot of the end-to-end distance versus time - :class: only-dark - -.. figure:: ../figures/level2/polymer-in-water/distance.png - :alt: plot of the end-to-end distance versus time - :class: only-light - -.. container:: figurelegend - - Figure: a) Evolution of the end-to-end distance of the PEG molecule - with time. The forcing starts at :math:`t = 30` ps. b) Evolution of the - gyration radius :math:`R_\text{gyr}` of the PEG molecule. - -.. container:: justify - - There is a follow-up to this polymer in water tutorial as :ref:`mda-label`, - where the trajectory is imported in Python using MDAnalysis. - -.. include:: ../../non-tutorials/accessfile.rst - -Going further with exercises -============================ - -.. include:: ../../non-tutorials/link-to-solutions.rst - -Extract the radial distribution function ----------------------------------------- - -.. container:: justify - - Extract the radial distribution functions (RDF or :math:`g(r)`) - between the oxygen atom of the water molecules - and the oxygen atom from the PEG molecule. Compare the rdf - before and after the force is applied to the PEG. - -.. figure:: ../figures/level2/polymer-in-water/RDF-dark.png - :alt: RDF g(r) for water and peg - :class: only-dark - -.. figure:: ../figures/level2/polymer-in-water/RDF-light.png - :alt: RDF g(r) for water and peg - :class: only-light - -.. container:: figurelegend - - Figure: Radial distribution function between the oxygen atoms - of water, as well as between the oxygen atoms of water and the - oxygen atoms of the PEG molecule. - -.. container:: justify - - Note the difference in the structure of the water before and after - the PEG molecule is stretched. This effect is described in - the 2017 publication by Liese et al. :cite:`liese2017hydration`. - -Add salt to the system ----------------------- - -.. container:: justify - - Realistic systems usually contain ions. Let us add some :math:`\text{Na}^+` and - :math:`\text{Cl}^-` ions to our current PEG-water system. - -.. container:: justify - - Add some :math:`\text{Na}^+` and - :math:`\text{Cl}^-` ions to the mixture using the method - of your choice. :math:`\text{Na}^+` ions are - characterised by their mass :math:`m = 22.98\,\text{g/mol}`, - their charge :math:`q = +1\,e`, and Lennard-Jones - parameters, :math:`\epsilon = 0.0469\,\text{kcal/mol}` - and :math:`\sigma = 0.243\,\text{nm}`, - and :math:`\text{Cl}^-` ions by their - mass :math:`m = 35.453\,\text{g/mol}`, - charge :math:`q = -1\,e` and Lennard-Jones - parameters, :math:`\epsilon = 0.15\,\text{kcal/mol}`, - and :math:`\sigma = 0.4045\,\text{nm}`. - -.. figure:: ../figures/level2/polymer-in-water/salt-exercise-dark.png - :alt: PEG in a NaCl solution - :class: only-dark - -.. figure:: ../figures/level2/polymer-in-water/salt-exercise-light.png - :alt: PEG in a NaCl solution - :class: only-light - -.. container:: figurelegend - - Figure: A PEG molecule in the electrolyte with :math:`\text{Na}^+` ions in - purple and :math:`\text{Cl}^-` ions in cyan. - -Evaluate the deformation of the PEG ------------------------------------ - -.. container:: justify - - Once the PEG is fully stretched, its structure differs from the - unstretched case. The deformation can be probed by extracting the typical - intra-molecular parameters, such as the typical angles of the dihedrals. - -.. container:: justify - - Extract the histograms of the angular distribution of the PEG dihedrals - in the absence and the presence of stretching. - -.. figure:: ../figures/level2/polymer-in-water/dihedral_angle-dark.png - :alt: PEG in a NaCl solution - :class: only-dark - -.. figure:: ../figures/level2/polymer-in-water/dihedral_angle-light.png - :alt: PEG in a NaCl solution - :class: only-light - -.. container:: figurelegend - - Figure: Probability distribution for the dihedral angle :math:`\phi`, for a stretched - and for an unstretched PEG molecule. diff --git a/docs/sphinx/source/tutorials/level3/free-energy-calculation.rst b/docs/sphinx/source/tutorials/level3/free-energy-calculation.rst deleted file mode 100644 index 5521a93ae..000000000 --- a/docs/sphinx/source/tutorials/level3/free-energy-calculation.rst +++ /dev/null @@ -1,844 +0,0 @@ -.. _umbrella-sampling-label: - -Free energy calculation -*********************** - -.. container:: hatnote - - Sampling a free energy barrier - -.. figure:: ../figures/level3/free-energy-calculation/avatar_light.webp - :height: 250 - :alt: Lennard Jones atoms simulated with LAMMPS - :class: only-light - :align: right - -.. figure:: ../figures/level3/free-energy-calculation/avatar_dark.webp - :height: 250 - :alt: Lennard Jones atoms simulated with LAMMPS - :class: only-dark - :align: right - -.. container:: justify - - The objective of this tutorial is to measure a free - energy profile of particles across a barrier potential - using two methods: free sampling - and umbrella sampling :cite:`kastner2011umbrella, allen2017computer, frenkel2023understanding`. - -.. container:: justify - - For simplicity and to reduce computation time, the barrier potential will - be imposed on the atoms with an additional force, mimicking the presence of - a repulsive area in the middle of the simulation box without the need to - simulate extra atoms. The procedure is valid for more complex systems and - can be adapted to many other situations, such as measuring the adsorption - barrier near an interface, or for calculating a translocation - barrier through a membrane :cite:`wilson1997adsorption, makarov2009computer, gravelle2021adsorption`. - -.. include:: ../../non-tutorials/recommand-lj.rst - -.. include:: ../../non-tutorials/cite.rst - -.. include:: ../../non-tutorials/2Aug2023.rst - -.. admonition:: What is free energy - :class: info - - The *free energy* refers to the potential energy of a system that - is available to perform work. In molecular simulations, it is - common to calculate free energy differences between different states - or conformations of a molecular system. This can be useful in understanding - the thermodynamics of a system, predicting reaction pathways, and - determining the stability of different molecular configurations. - -Method 1: Free sampling -======================= - -.. container:: justify - - The most direct way to calculate a free energy profile is to extract - the partition function from a classic (i.e., unbiased) molecular - dynamics simulation, and then estimate the Gibbs free - energy using - -.. math:: - - \Delta G = -RT \ln(p/p_0), - -.. container:: justify - - where :math:`\Delta G` is the free energy difference, - :math:`R` is the gas constant, - :math:`T` is the temperature, - :math:`p` is the pressure, - and :math:`p_0` is a reference pressure. - As an illustration, let us apply this method to an - extremely simple configuration that consists of a few - particles diffusing in a box in the presence of a position-dependent repelling - force that makes the center of the box a relatively unfavorable area to explore. - -Basic LAMMPS parameters ------------------------ - -.. container:: justify - - Create a folder named *FreeSampling/*, and create an input script - named *input.lammps* in it. Copy the following lines into it: - -.. code-block:: lammps - - variable sigma equal 3.405 # Angstrom - variable epsilon equal 0.238 # Kcal/mol - variable U0 equal 1.5*${epsilon} # Kcal/mol - variable dlt equal 0.5 # Angstrom - variable x0 equal 5.0 # Angstrom - - units real - atom_style atomic - pair_style lj/cut 3.822 - pair_modify shift yes - boundary p p p - -.. container:: justify - - Here, we start by defining variables for the Lennard-Jones - interaction :math:`\sigma` and :math:`\epsilon` and for - the repulsive potential :math:`U (x)`: :math:`U_0`, :math:`\delta`, and :math:`x_0`, - see the analytical expression below. With :math:`U_0 = 1.5 \epsilon = 0.36\,\text{kcal/mol}`, - :math:`U_0` is of the same order as the thermal energy :math:`k_\text{B} T = 0.24,\text{kcal/mol}`, - where :math:`k_\text{B} = 0.002\,\text{kcal/mol/K}` is the Boltzmann constant - and :math:`T = 119.8\,\text{K}` (see below). In this case, particles are expected - to regularly overcome the energy barrier thanks to the thermal agitation. - -.. container:: justify - - The value of 3.822 for the cut-off was chosen to - create a WCA, purely repulsive, potential :cite:`weeks1971role`. It was calculated - as :math:`2^{1/6} \times 3.405` where - :math:`3.405 = \sigma`. - -.. container:: justify - - The system of unit *real*, for which energy is in kcal/mol, distance in Ångstrom, - or time in femtosecond, has been chosen for practical reasons: - the WHAM algorithm used in the second - part of the tutorial automatically assumes the energy to - be in kcal/mol. Atoms will interact through a - Lennard-Jones potential with a cut-off equal to - :math:`\sigma \times 2 ^ {1/6}` (i.e. a WCA repulsive - potential). The potential is shifted to be equal to 0 at - the cut-off using the *pair_modify*. - -System creation and settings ----------------------------- - -.. container:: justify - - Let us define the simulation block and randomly add atoms - by adding the following lines into *input.lammps*: - -.. code-block:: lammps - - region myreg block -25 25 -5 5 -25 25 - create_box 1 myreg - create_atoms 1 random 60 341341 myreg overlap 1.5 maxtry 50 - - mass * 39.95 - pair_coeff * * ${epsilon} ${sigma} - neigh_modify every 1 delay 4 check yes - -.. container:: justify - - The values for the Lennard-Jones parameters (:math:`\sigma` - and :math:`\epsilon`) and - the mass (:math:`m = 39.95`) are typical of argon. - -.. container:: justify - - The variables :math:`U_0`, - :math:`\delta`, - and :math:`x_0` defined in the previous subsection are used here to create - the repulsive potential, restricting the atoms from exploring the center of the box: - -.. math:: - - U(x) = U_0 \left[ \arctan \left( \dfrac{x+x_0}{\delta} \right) - \arctan \left(\dfrac{x-x_0}{\delta} \right) \right]. - -.. container:: justify - - From the derivative of the - potential with respect to :math:`x`, we obtain the expression - for the force that will be imposed on the atoms: - -.. math:: - - F(x)= \dfrac{U_0}{\delta} \left[ \dfrac{1}{(x-x_0)^2/\delta^2+1} - \dfrac{1}{(x+x_0)^2/\delta^2+1} \right]. - -.. container:: justify - - The potential and force along the :math:`x` - axis resembles: - -.. figure:: ../figures/level3/free-energy-calculation/potential.png - :alt: Imposed potential - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/potential-dm.png - :alt: Averaged density profile - :class: only-dark - -.. container:: figurelegend - - Figure: Potential :math:`U (x)` (a) and force :math:`F (x)` (b) imposed to the atoms as a function of the coordinate :math:`x`. - -**Energy minimization and equilibration** - -.. container:: justify - - Let us apply energy minimization to the system, - and then impose the force :math:`F(x)` to all of - the atoms in the simulation using the *addforce* command. - Add the following lines to *input.lammps*: - -.. code-block:: lammps - - minimize 1e-4 1e-6 100 1000 - reset_timestep 0 - - variable U atom ${U0}*atan((x+${x0})/${dlt}) & - -${U0}*atan((x-${x0})/${dlt}) - variable F atom ${U0}/((x-${x0})^2/${dlt}^2+1)/${dlt} & - -${U0}/((x+${x0})^2/${dlt}^2+1)/${dlt} - fix myadf all addforce v_F 0.0 0.0 energy v_U - -.. container:: justify - - Finally, let us combine the *fix nve* with a *Langevin* - thermostat and run a molecular dynamics simulation. With - these two commands, the MD simulation is effectively in the - NVT ensemble: constant number of atoms :math:`N`, constant - volume :math:`V`, and constant temperature :math:`T`. Let us - perform an equilibration of 500000 steps in total, - using a timestep of :math:`2\,\text{fs}` - (i.e. a total duration of :math:`1\,\text{ns}`). - -.. container:: justify - - To make sure that :math:`1\,\text{ns}` is long enough, we will - record the evolution of the number of atoms in the central - (energetically unfavorable) region called *mymes*: - -.. code-block:: lammps - - fix mynve all nve - fix mylgv all langevin 119.8 119.8 50 1530917 - - region mymes block -${x0} ${x0} INF INF INF INF - variable n_center equal count(all,mymes) - fix myat all ave/time 10 50 500 v_n_center file density_evolution.dat - - timestep 2.0 - thermo 10000 - run 500000 - -Run and data acquisition ------------------------- - -.. container:: justify - - Finally, let us record the density profile of the atoms - along the :math:`x` axis using the *ave/chunk* command. - A total of 10 density profiles will be printed. The step count is - reset to 0 to synchronize with the output times of - *fix density/number*, and the *fix myat* is canceled (it has to be - canceled before a reset time): - -.. code-block:: lammps - - unfix myat - reset_timestep 0 - - compute cc1 all chunk/atom bin/1d x 0.0 1.0 - fix myac all ave/chunk 10 400000 4000000 & - cc1 density/number & - file density_profile.dat - dump mydmp all atom 200000 dump.lammpstrj - - thermo 100000 - run 4000000 - -.. container:: justify - - This simulation with a total duration of :math:`9\,\text{ns}` needs a few - minutes to complete. Feel free to increase the - duration of the last run for smoother results. - -.. figure:: ../figures/level3/free-energy-calculation/system-light.png - :alt: Lennard jones atoms simulated with LAMMPS MD code - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/system-dark.png - :alt: Lennard jones atoms simulated with LAMMPS MD code - :class: only-dark - -.. container:: figurelegend - - Figure: Snapshot of the system. Notice that the density of atoms is lower in the central - part of the box, due to the additional force :math:`F (x)`. - -Data analysis --------------- - -.. container:: justify - - First, let us ensure that the initial equilibration of :math:`1\,\text{ns}` - is long enough by examining the *density_evolution.dat* file. - -.. figure:: ../figures/level3/free-energy-calculation/density_evolution.png - :alt: Number of particles in the central region as a function of time - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/density_evolution-dm.png - :alt: Number of particles in the central region as a function of time - :class: only-dark - -.. container:: figurelegend - - Figure: Evolution of the number of atoms in the central region during equilibration. - -.. container:: justify - - Here, we can see that the number of atoms in the - central region, :math:`n_\mathrm{central}`, evolves near its - equilibrium value (which is close to 0) after about :math:`0.1\,\text{ns}`. - -.. container:: justify - - One can also look at the density profile, which shows that the density in the - center of the box is about two orders of magnitude lower than inside - the reservoir. - -.. figure:: ../figures/level3/free-energy-calculation/density_profile.png - :alt: Averaged density profile - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/density_profile-dm.png - :alt: Averaged density profile - :class: only-dark - -.. container:: figurelegend - - Figure: Fluid density :math:`\rho` along the :math:`x` direction in the presence - of a repulsive potential with :math:`U_0 = 1.5 \epsilon`. The reference density is :math:`\rho_\text{bulk} = 0.0033~\text{Å}^{-3}`. - -.. container:: justify - - Then, let us plot :math:`-R T \ln(\rho/\rho_\mathrm{bulk})` and compare it - with the imposed (reference) potential :math:`U`. - -.. figure:: ../figures/level3/free-energy-calculation/freesampling-potential-light.png - :alt: Averaged density profile - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/freesampling-potential-dark.png - :alt: Averaged density profile - :class: only-dark - -.. container:: figurelegend - - Figure: Calculated potential :math:`-R T \ln(\rho/\rho_\mathrm{bulk})` - compared to the imposed potential with :math:`U_0 = 1.5 \epsilon`. - The calculated potential is in blue. - -.. container:: justify - - The agreement with the expected energy profile is reasonable, - despite some noise in the central part (where fewer data points are - available due to the repulsive potential). - -The limits of free sampling ---------------------------- - -.. container:: justify - - If we increase the value of :math:`U_0`, the average number of atoms in the central - region will decrease, making it difficult to obtain a good resolution for the - free energy profile. For instance, when we run the same simulation using - :math:`U_0 = 10 \epsilon`, which corresponds to a situation - where :math:`U_0 \approx 10 k_\text{B} T`, not a single atom explores the central - part of the simulation box during the 8 ns of simulation. In this case, using - an enhanced sampling method is preferred; see the next section. - -.. figure:: ../figures/level3/free-energy-calculation/density_profile_large_potential-light.png - :alt: Averaged density profile with large potential - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/density_profile_large_potential-dark.png - :alt: Averaged density profile large potential - :class: only-dark - -.. container:: figurelegend - - Figure: Fluid density :math:`\rho` along the :math:`x` direction in the presence - of a repulsive potential with :math:`U_0 = 10 \epsilon`. - -.. container:: justify - - In that case, it is better to use the umbrella sampling method - to extract free energy profiles, see the next section. - -Method 2: Umbrella sampling -=========================== - -.. container:: justify - - Umbrella sampling is a biased molecular dynamics method in which additional forces are added to a chosen atom to - force it to explore the entire space, including the more unfavorable areas of - the system :cite:`kastner2011umbrella, allen2017computer, frenkel2023understanding`. - -.. container:: justify - - To encourage one of the atoms to explore the central region of the box, we apply a potential :math:`V` and force it to move - along the :math:`x`-axis. The chosen path is called the axis of reaction. Several simulations (or windows) will be conducted - with varying parameters for the applied biasing. The results will be analyzed using the weighted histogram analysis - method (WHAM) :cite:`kumar1992weighted`, which allows for the removal of the biasing effect and ultimately deduces - the unbiased free energy profile. - -LAMMPS input script -------------------- - -.. container:: justify - - Create a new folder called *BiasedSampling/*, and create a new input file - named *input.lammps* in it. Copy the following lines into *input.lammps*: - -.. code-block:: lammps - - variable sigma equal 3.405 # Angstrom - variable epsilon equal 0.238 # Kcal/mol - variable U0 equal 10*${epsilon} # Kcal/mol - variable dlt equal 0.5 # Angstrom - variable x0 equal 5.0 # Angstrom - variable k equal 1.5 # Kcal/mol/Angstrom^2 - - units real - atom_style atomic - pair_style lj/cut 3.822 # 2^(1/6) * 3.405 WCA potential - pair_modify shift yes - boundary p p p - - region myreg block -25 25 -5 5 -25 25 - create_box 2 myreg - create_atoms 2 single 0 0 0 - create_atoms 1 random 59 341341 myreg overlap 1.5 maxtry 50 - - mass * 39.948 - pair_coeff * * ${epsilon} ${sigma} - neigh_modify every 1 delay 4 check yes - group topull type 2 - - minimize 1e-4 1e-6 100 1000 - reset_timestep 0 - - variable U atom ${U0}*atan((x+${x0})/${dlt}) & - -${U0}*atan((x-${x0})/${dlt}) - variable F atom ${U0}/((x-${x0})^2/${dlt}^2+1)/${dlt} & - -${U0}/((x+${x0})^2/${dlt}^2+1)/${dlt} - fix pot all addforce v_F 0.0 0.0 energy v_U - - fix mynve all nve - fix mylgv all langevin 119.8 119.8 50 1530917 - timestep 2.0 - thermo 100000 - run 500000 - reset_timestep 0 - - dump mydmp all atom 1000000 dump.lammpstrj - -.. container:: justify - - So far, this code resembles that of Method 1, - except for the additional particle of type 2. Particles of type 1 and 2 - are identical, having the same mass and Lennard-Jones parameters. However, - the particle of type 2 will additionally be exposed to the biasing potential :math:`V`. - -.. container:: justify - - Let us create a loop with 50 steps, and move progressively - the center of the bias potential by an increment of 0.1 nm. - Add the following lines to *input.lammps*: - -.. code-block:: lammps - - variable a loop 50 - label loop - variable xdes equal ${a}-25 - variable xave equal xcm(topull,x) - fix mytth topull spring tether ${k} ${xdes} 0 0 0 - run 200000 - fix myat1 all ave/time 10 10 100 v_xave v_xdes & - file data-k1.5/position.${a}.dat - run 1000000 - unfix myat1 - next a - jump SELF loop - -.. container:: justify - - A folder named *data-k1.5/* needs to be created within *BiasedSampling/*. - -.. container:: justify - - The spring command serves to impose the - additional harmonic potential with the spring constant :math:`k`. - Note that the value of :math:`k` should be chosen with care, - if :math:`k` is too small, the particle won't follow the biasing potential - center, if :math:`k` is too large, there will be no overlapping between the - different windows. See the side note named *on the choice of k* below. - -.. container:: justify - - The center of the harmonic potential :math:`x_\text{des}` - successively takes values from -25 to 25. For each value of - :math:`x_\text{des}`, an equilibration step of 0.4 ns is - performed, followed by a step of 2 ns during which the - position along :math:`x` of the particle is saved in data - files (one data file per value of :math:`x_\text{des}`). You - can always increase the duration of the runs for better samplings. - -WHAM algorithm --------------- - -.. container:: justify - - To generate the free energy profile from the density distribution, - let us use the WHAM algorithm as implemented by Alan Grossfield :cite:`grossfieldimplementation`. - -.. container:: justify - - You can download it from |Grossfield| website, and compile it using: - -.. |Grossfield| raw:: html - - Alan Grossfield - -.. code-block:: bash - - cd wham - make clean - make - -.. container:: justify - - The compilation creates an executable called *wham* that you can - copy in the *BiasedSampling/* folder. Alternatively, use - the |wham-version| I have downloaded, or try your luck with the version - I precompiled: |wham-precompiled|. - -.. |wham-version| raw:: html - - version 2.0.11 - -.. |wham-precompiled| raw:: html - - precompiled wham - -.. container:: justify - - In order to apply the WHAM algorithm to our simulation, we - first need to create a metadata file. This file simply - contains - -.. container:: justify - - - the paths to all the data files, - - the value of :math:`x_\text{des}`, - - and the values of :math:`k`. - -.. container:: justify - - To generate the *metadata.txt* file, you can run this Python script - from the *BiasedSampling/* folder: - -.. code-block:: python - - import os - - k=1.5 - folder='data-k1.5/' - - f = open("metadata.dat", "w") - for n in range(-50,50): - datafile=folder+'position.'+str(n)+'.dat' - if os.path.exists(datafile): - # read the imposed position is the expected one - with open(datafile) as g: - _ = g.readline() - _ = g.readline() - firstline = g.readline() - imposed_position = firstline.split(' ')[-1][:-1] - # write one file per file - f.write(datafile+' '+str(imposed_position)+' '+str(k)+'\n') - f.close() - -.. container:: justify - - Here, :math:`k` is in kcal/mol. - The generated file named *metadata.dat* looks like this: - -.. code-block:: bash - - ./data-k1.5/position.1.dat -24 1.5 - ./data-k1.5/position.2.dat -23 1.5 - ./data-k1.5/position.3.dat -22 1.5 - (...) - ./data-k1.5/position.48.dat 23 1.5 - ./data-k1.5/position.49.dat 24 1.5 - ./data-k1.5/position.50.dat 25 1.5 - -.. container:: justify - - Alternatively, you can download this |download_metadata| file. - Then, simply run the following command in the terminal: - -.. |download_metadata| raw:: html - - metadata.dat - -.. code-block:: bash - - ./wham -25 25 50 1e-8 119.8 0 metadata.dat PMF.dat - -.. container:: justify - - where -25 and 25 are the boundaries, 50 is the number of bins, - 1e-8 the tolerance, and 119.8 the temperature. A file named - PMF.dat has been created and contains the free energy - profile in Kcal/mol. - -**Results** - -.. container:: justify - - Again, one can compare the result of the PMF with the imposed potential :math:`U`, - which shows that the agreement is excellent. - -.. figure:: ../figures/level3/free-energy-calculation/freeenergy.png - :alt: Result of the umbrella sampling - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/freeenergy-dm.png - :alt: Result of the umbrella sampling - :class: only-dark - -.. container:: figurelegend - - Figure: Calculated potential using umbrella sampling compared to - the imposed potential with :math:`U_0 = 10 \epsilon`. The calculated potential is in blue. - -.. container:: justify - - We can see that the agreement is quite good despite the very short calculation time - and the very high value for the energy barrier. Obtaining the same - results with Free Sampling would require performing extremely long - and costly simulations. - -.. include:: ../../non-tutorials/accessfile.rst - -Side note: on the choice of k ------------------------------ - -.. container:: justify - - As already stated, one difficult part of umbrella sampling is to choose the value of :math:`k`. - Ideally, you want the biasing potential to be strong enough to force - the chosen atom to move along the axis, and you also want the - fluctuations of the atom position to be large enough to - have some overlap in the density probability of two - neighbor positions. Here, 3 different values of :math:`k` are being tested. - -.. figure:: ../figures/level3/free-energy-calculation/overlap-light.png - :alt: Averaged density profile - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/overlap-dark.png - :alt: Averaged density profile - :class: only-dark - -.. container:: figurelegend - - Figure: Density probability for each run with :math:`k = 0.15\,\text{Kcal}/\text{mol}/Å^2` (a), - :math:`k = 1.5\,\text{Kcal}/\text{mol}/Å^2` (b), - and :math:`k = 15\,\text{Kcal}/\text{mol}/Å^2` (c). - -.. container:: justify - - If :math:`k` is too small, the biasing potential is too weak to - force the particle to explore the - region of interest, making it impossible to reconstruct the PMF. - -.. container:: justify - - If :math:`k` is too large, the biasing potential is too large - compared to the potential one wants to probe, which reduces the - sensitivity of the method. - -Going further with exercises -============================ - -.. include:: ../../non-tutorials/link-to-solutions.rst - -The binary fluid that won't mix -------------------------------- - -.. container:: justify - - **1 - Create the system** - - Create a molecular simulation with two species of respective types 1 and 2. - Apply different potentials :math:`U1` and :math:`U2` on particles of - types 1 and 2, respectively, so that particles of type 1 are excluded - from the center of the box, while at the same time particles - of type 2 are excluded from the rest of the box. - -.. figure:: ../figures/level3/free-energy-calculation/exercice2-light.png - :alt: Particles of type 1 and 2 separated by two different potentials - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/exercice2-dark.png - :alt: Particles of type 1 and 2 separated by two different potentials - :class: only-dark - -.. container:: figurelegend - - Figure: Particles of type 1 and 2 separated by two different potentials. - -.. container:: justify - - **2 - Measure the PMFs** - - Using the same protocol as the one used in the tutorial - (i.e. umbrella sampling with the wham algorithm), - extract the PMF for each particle type. - -.. figure:: ../figures/level3/free-energy-calculation/exercice-binary-light.png - :alt: PMF in the presence of binary species - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/exercice-binary-dark.png - :alt: PMF in the presence of binary species - :class: only-dark - -.. container:: figurelegend - - Figure: PMFs calculated for both atom types. - -Particles under convection --------------------------- - -.. container:: justify - - Use a similar simulation as the one from the tutorial, - with a repulsive potential in the center - of the box. Add force to the particles - and force them to flow in the :math:`x` direction. - -.. container:: justify - - Re-measure the potential in the presence of the flow, and observe the difference - with the reference case in the absence of flow. - -.. figure:: ../figures/level3/free-energy-calculation/exercice-convection-light.png - :alt: PMF in the presence of forcing - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/exercice-convection-dark.png - :alt: PMF in the presence of forcing - :class: only-dark - -.. container:: figurelegend - - Figure: PMF calculated in the presence of a net force that is inducing - the convection of the particles from left to right. - -Surface adsorption of a molecule --------------------------------- - -.. container:: justify - - Apply umbrella sampling to calculate the free energy profile - of ethanol in the direction normal to a crystal solid surface - (here made of sodium chloride). Find the |topology-ethanol| - and |parameter-ethanol|. - -.. |topology-ethanol| raw:: html - - topology files - -.. |parameter-ethanol| raw:: html - - parameter file - - -.. container:: justify - - Use the following lines for starting the *input.lammps*: - -.. code-block:: lammps - - units real # style of units (A, fs, Kcal/mol) - atom_style full # molecular + charge - bond_style harmonic - angle_style harmonic - dihedral_style harmonic - boundary p p p # periodic boundary conditions - pair_style lj/cut/coul/long 10 # cut-off 1 nm - kspace_style pppm 1.0e-4 - pair_modify mix arithmetic tail yes - -.. container:: justify - - The PMF normal to a solid wall serves to indicate the free energy of adsorption, - which can be calculated from the difference between the PMF far - from the surface, and the PMF at the wall. - -.. figure:: ../figures/level3/free-energy-calculation/ethanol-light.png - :alt: Ethanol molecule next to NaCl - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/ethanol-dark.png - :alt: Ethanol molecule next to NaCl - :class: only-dark - -.. container:: figurelegend - - Figure: A single ethanol molecule next to a crystal NaCl(100) wall. - -.. container:: justify - - The PMF shows a minimum near the solid surface, which indicates a good - affinity between the wall and the molecule. - -.. figure:: ../figures/level3/free-energy-calculation/exercice-ethanol-light.png - :alt: PMF for ethanol molecule next to NaCl - :class: only-light - -.. figure:: ../figures/level3/free-energy-calculation/exercice-ethanol-dark.png - :alt: PMF for ethanol molecule next to NaCl - :class: only-dark - -.. container:: figurelegend - - Figure: PMF for a single ethanol molecule next to a NaCl - solid surface. The position of the wall is in :math:`x=0`. - The arrow highlights the difference between the energy of the - molecule when adsorbed to the solid surface, and - the energy far from the surface. This difference corresponds to the - free energy of adsorption. - -.. container:: justify - - Alternatively to using ethanol, feel free to download the molecule of your choice, for - instance from the Automated Topology Builder (ATB). Make your life simpler - by choosing a small molecule like CO2. diff --git a/docs/sphinx/source/tutorials/level3/reactive-silicon-dioxide.rst b/docs/sphinx/source/tutorials/level3/reactive-silicon-dioxide.rst deleted file mode 100644 index 2e09e16eb..000000000 --- a/docs/sphinx/source/tutorials/level3/reactive-silicon-dioxide.rst +++ /dev/null @@ -1,681 +0,0 @@ -.. _reactive-silicon-dioxide-label: - -Reactive silicon dioxide -************************ - -.. container:: hatnote - - Simulating a chemically reactive structure - -.. figure:: ../figures/level3/reactive-silicon-dioxide/SiO_gif_light.webp - :height: 250 - :alt: Figure showing silicon dioxide structure with colored charges as simulated with lammps and reaxff - :class: only-light - :align: right - -.. figure:: ../figures/level3/reactive-silicon-dioxide/SiO_gif_dark.webp - :height: 250 - :alt: Figure showing silicon dioxide structure with colored charges as simulated with lammps and reaxff - :class: only-dark - :align: right - -.. container:: justify - - The objective of this tutorial is to use a - reactive force field (ReaxFF :cite:`van2001reaxff, zou2012investigation`) - to calculate the partial charges of a system undergoing - deformation, as well as chemical bond formation and breaking. - -.. container:: justify - - The system simulated here is a block of silicon dioxide (:math:`\text{SiO}_2`) that is deformed - until rupture. Particular attention is given to the evolution of the atomic charges - during the deformation of the structure, and - the chemical reactions occurring due to the deformation - are tracked over time. - -.. include:: ../../non-tutorials/recommand-lj.rst - -.. include:: ../../non-tutorials/cite.rst - -.. include:: ../../non-tutorials/2Aug2023.rst - -Prepare and relax -================= - -.. container:: justify - - Create a folder, name it *RelaxSilica/*, - and |download_silica_data| the initial topology of a small - amorphous silica structure. - -.. |download_silica_data| raw:: html - - download - -.. admonition:: About the initial structure - :class: info - - The system was created by temperature annealing using another force field - named |download_SiO.1990.vashishta| - :cite:`vashishta1990interaction`. In case you are - interested in the input creation, the files - used for creating the initial topology is available - |lammps_input_creating|. - -.. |download_SiO.1990.vashishta| raw:: html - - vashishta - -.. |lammps_input_creating| raw:: html - - here - -.. container:: justify - - If you open the *silica.data* file, you will see in the Atoms section that - all silicon atoms have the same charge :math:`q = 1.1\,\text{e}`, - and all oxygen atoms the charge :math:`q = -0.55\,\text{e}`. - This is common with classical force fields and will change once - ReaxFF is used. - -.. container:: justify - - The first action we need to perform here is to relax - the structure with ReaxFF, which we are gonna do using molecular - dynamics. To make sure that the system equilibrates - nicely, let us track some parameters over time. - -.. container:: justify - - Create an input file called *input.lammps* in *RelaxSilica/*, - and copy the following lines into it: - -.. code-block:: lammps - - units real - atom_style full - - read_data silica.data - - mass 1 28.0855 # Si - mass 2 15.999 # O - -.. container:: justify - - So far, the input is very similar to what was seen - in the previous tutorials. Some basic parameters are - defined (*units*, *atom_style* and *masses*), and - the *.data* file is imported by the *read_data* command. - Now, let us copy three crucial lines into the *input.lammps* file: - -.. code-block:: lammps - - pair_style reaxff NULL safezone 3.0 mincap 150 - pair_coeff * * reaxCHOFe.ff Si O - fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400 - -.. container:: justify - - Here, the *pair_style reaxff* is used with no control file. - The *safezone* and *mincap* keywords have been added - to avoid memory allocation issues, which sometimes can trigger - the segmentation faults and *bondchk* failed errors. - -.. container:: justify - - The *pair_coeff* uses - the |reaxCHOFe| file, which must be downloaded and saved within - *RelaxSilica/*. For consistency with the masses and the *silica.data* file, - the atoms of type 1 are set as silicon (Si), - and the atoms of type 2 as oxygen (O). - -.. container:: justify - - Finally, the *fix qeq/reaxff* is used to perform charge equilibration :cite:`rappe1991charge`. - The charge equilibration occurs at every step. The values 0.0 and 10.0 - are the low and the high cutoffs, respectively, and :math:`1.0 \text{e} -6` is a - tolerance. Finally, *maxiter* sets an upper limit to the number of attempts to - equilibrate the charge. - -.. admonition:: Note - :class: info - - If the charge does not - properly equilibrate despite the 400 attempts, a warning will appear. Such warnings - are likely to appear at the beginning of the simulation if the initial charges - are too far from the equilibrium values. - -.. |reaxCHOFe| raw:: html - - reaxCHOFe.ff - -.. container:: justify - - Then, let us add some commands to the *input.lammps* file - to measure the evolution of the charges during the simulation: - -.. code-block:: lammps - - group grpSi type 1 - group grpO type 2 - variable qSi equal charge(grpSi)/count(grpSi) - variable qO equal charge(grpO)/count(grpO) - -.. container:: justify - - Let us also print the charge in the *.log* file by using *thermo_style*, - and create a *.lammpstrj* file for visualization. - Add the following lines into the *input.lammps*: - -.. code-block:: lammps - - thermo 5 - thermo_style custom step temp etotal press vol v_qSi v_qO - dump dmp all custom 100 dump.lammpstrj id type q x y z - -.. container:: justify - - Let us also use the *fix reaxff/species* to evaluate what - species are present within the simulation. It will be useful later when - the system is deformed and some bonds are broken: - -.. code-block:: lammps - - fix myspec all reaxff/species 5 1 5 species.log element Si O - -.. container:: justify - - Here, the information will be printed every 5 steps in a - file named *species.log*. - -.. container:: justify - - Let us perform a very short run using the anisotropic NPT command - and relax the density of the system. - -.. code-block:: lammps - - velocity all create 300.0 3482028 - fix mynpt all npt temp 300.0 300.0 100 aniso 1.0 1.0 1000 - timestep 0.5 - - run 5000 - - write_data silica-relaxed.data - -.. container:: justify - - Run the *input.lammps* file using LAMMPS. As seen from *species.log*, - only one species is detected, called *Si192O384*, representing the entire system. - -.. container:: justify - - As the simulation progresses, you can see that the charges of the atoms are - fluctuating since the charge of every individual atom is adjusting to its - local environment in real time. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/average-charge.png - :alt: Charge of silica during equilibration with reaxff and LAMMPS - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/average-charge-dm.png - :alt: Charge of silica during equilibration with reaxff and LAMMPS - :class: only-dark - -.. container:: figurelegend - - Figure: Average charge per atom of the silicon (a) and oxygen (b) - atoms during equilibration, as given by the - *v_qSi* and *v_qO* variables. - -.. container:: justify - - Additionally, the average charge of the atoms is strongly fluctuating - at the beginning of the simulation. This early fluctuation correlates - with a rapid volume change of the box, during which - the inter-atomic distances are expected to quickly change. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/volume.png - :alt: volume of the system with reaxff and LAMMPS - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/volume-dm.png - :alt: volume of the system with reaxff and LAMMPS - :class: only-dark - -.. container:: figurelegend - - Figure: Volume of the system as a function of time. - -.. container:: justify - - Since each atom has a charge that depends on its local environment, - the charge values are expected to be different for every atom in the system. - We can plot the charge distribution :math:`P(q)`, using the charge values - printed in the *.lammptrj* file. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/distribution-charge.png - :alt: Distribution charge of silica and oxygen during equilibration with reaxff - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/distribution-charge-dm.png - :alt: Distribution charge of silica and oxygen during equilibration with reaxff - :class: only-dark - -.. container:: figurelegend - - Figure: Probability distribution of charge of silicon (positive, blue) - and oxygen (negative, orange) atoms during equilibration. - -.. container:: justify - - Using VMD and coloring the atoms by their charges, one can see that - the atoms with the extreme-most charges are located at defects in the - amorphous structure (here at the positions of the dangling oxygen groups). - -.. figure:: ../figures/level3/reactive-silicon-dioxide/silicon-light.png - :alt: Amorphous silica colored by charges using VMD - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/silicon-dark.png - :alt: Amorphous silica colored by charges using VMD - :class: only-dark - -.. container:: figurelegend - - Figure: A slice of the amorphous silica, where atoms are colored by their - charges. Dangling oxygen groups appear in greenish, bulk Si atoms with a - charge of about :math:`1.8~\text{e}` appear in red/orange, and bulk O atoms with a charge of - about :math:`-0.9~\text{e}` appear in blue. To color the atoms by their - charge using VMD, use *Charge* as the coloring method in the representation - windows, and then tune the *Color scale* in the *Color control windows*. - -Deform the structure -==================== - -.. container:: justify - - Let us apply a deformation to the structure to force some - :math:`\text{Si}-\text{O}` bonds to break (and eventually re-assemble). - -.. container:: justify - - Next to *RelaxSilica/*, create a folder, call it *Deform/* and create a - file named *input.lammps* in it. Copy the same lines as previously in - *input.lammps*: - -.. code-block:: lammps - - units real - atom_style full - - read_data ../RelaxSilica/silica-relaxed.data - - mass 1 28.0855 # Si - mass 2 15.999 # O - - pair_style reaxff NULL safezone 3.0 mincap 150 - pair_coeff * * ../RelaxSilica/reaxCHOFe.ff Si O - fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400 - -.. container:: justify - - The only differences with the previous *input.lammps* file are the - paths to the *.data* and *.ff* files located within *RelaxSilica/*. - Copy the following lines as well: - -.. code-block:: lammps - - group grpSi type 1 - group grpO type 2 - variable qSi equal charge(grpSi)/count(grpSi) - variable qO equal charge(grpO)/count(grpO) - - thermo 5 - thermo_style custom step temp etotal press vol v_qSi v_qO - dump dmp all custom 100 dump.lammpstrj id type q x y z - - fix myspec all reaxff/species 5 1 5 species.log element Si O - -.. container:: justify - - Then, let us use *fix nvt* instead of *fix npt* to apply a - thermostat but no barostat: - -.. code-block:: lammps - - fix mynvt all nvt temp 300.0 300.0 100 - timestep 0.5 - -.. admonition:: Note - :class: info - - Here, no barostat is used because the box volume will be imposed by - the *fix deform*. - -.. container:: justify - - Let us run for 5000 steps without deformation, then apply the *fix deform* - for elongating progressively the box along *x* during 25000 steps. Add the - following line to *input.lammps*: - -.. code-block:: lammps - - run 5000 - - fix mydef all deform 1 x erate 5e-5 - - run 25000 - - write_data silica-deformed.data - -.. container:: justify - - Run the *input.lammps* file using LAMMPS. During the deformation, the charge values progressively evolve until the structure - eventually breaks down. After the structure breaks down, the charges - equilibrate near new average values that differ from the starting averages. - The difference between the initial and the final charges can be explained by - the presence of defects as well as new solid/vacuum interfaces, and the fact - that surface atoms typically have different charges compared to bulk atoms. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-charge.png - :alt: Charge of silica during deformation of the silicon oxide with LAMMPS and reaxff - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-charge-dm.png - :alt: Charge of silica during deformation of the silicon oxide with LAMMPS and reaxff - :class: only-dark - -.. container:: figurelegend - - Figure: Evolution of the average charge per atom of the silicon :math:`q_\text{Si}` - (a) and oxygen :math:`q_\text{O}` - (b) over time :math:`t`. The - vertical dashed lines mark the beginning of the deformation, and the horizontal - dashed lines denote the initial values for the average charge. - -.. container:: justify - - There is also a strong increase in temperature during the rupture of the - material. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-temperature.png - :alt: temperature of silica during deformation of the silicon oxide with LAMMPS and reaxff - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-temperature-dm.png - :alt: temperature of silica during deformation of the silicon oxide with LAMMPS and reaxff - :class: only-dark - -.. container:: figurelegend - - Figure: Evolution of the temperature :math:`T` of the silica system over time - :math:`t`. The material ruptures - near :math:`t = 10~\text{ps}`. - -.. container:: justify - - At the end of the deformation, one can visualize the broken material using - VMD. Notice the different charge values of the atoms located near the vacuum - interfaces, compared to the atoms located in the bulk of the material. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-light.png - :alt: Deformed amorphous silica colored by charges using VMD - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-dark.png - :alt: Deformed amorphous silica colored by charges using VMD - :class: only-dark - -.. container:: figurelegend - - Figure: Amorphous silicon oxide after deformation. The atoms are colored by - their charges. Dangling oxygen groups appear in greenish, bulk Si atoms with - a charge of about :math:`1.8~\text{e}` appear in red/orange, and bulk O atoms with a charge - of about :math:`-0.9~\text{e}`` appear in blue. - -.. container:: justify - - One can have a look at the charge distribution after deformation, - as well as during the deformation. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-distribution-charge.png - :alt: Distribution charge of silica and oxygen during equilibration with reaxff - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/deformed-distribution-charge-dm.png - :alt: Distribution charge of silica and oxygen during equilibration with reaxff - :class: only-dark - -.. container:: figurelegend - - Figure: Distribution of charge of silicon (positive, blue) and oxygen (negative, orange) - after deformation. The stars correspond to the charge distribution during deformation. - -.. container:: justify - - As expected, the final charge distribution slightly differs from the - previously calculated. In my case, no new species were formed during the - simulation, as can be seen from the *species.log* file: - -.. code-block:: lammps - - # Timestep No_Moles No_Specs Si192O384 - 5 1 1 1 - (...) - # Timestep No_Moles No_Specs Si192O384 - 30000 1 1 1 - -.. container:: justify - - Sometimes, :math:`\text{O}_2` molecules are formed during the - deformation. If this is the case, the *species.log* file will look like: - -.. code-block:: lammps - - # Timestep No_Moles No_Specs Si192O384 - 5 1 1 1 - (...) - # Timestep No_Moles No_Specs Si192O382 O2 - 30000 1 1 1 1 - -Decorate the surface -==================== - -.. container:: justify - - In ambient conditions, some of the surface :math:`\text{SiO}_2` atoms are chemically - passivated by forming covalent bonds with hydrogen (H) atoms :math:`sulpizi2012silica`. - Let us add hydrogen atoms randomly to the cracked silica and observe how - the system evolves over time. - -.. container:: justify - - Next to *RelaxSilica/* and *Deform/*, create a folder, and call it *Decorate/*. - Then, let us modify the previously generated data file - *silica-deformed.data* and make space for a third atom type. - Copy *silica-deformed.data* from the *Deform/* folder, - and modify the first lines as follow: - -.. code-block:: lammps - - 576 atoms - 3 atom types - - -12.15958814509652 32.74516585669389 xlo xhi - 2.316358282925984 18.26921942866687 ylo yhi - 1.3959542953413138 19.189623416252907 zlo zhi - - Masses - - 1 28.0855 - 2 15.999 - 3 1.008 - - (...) - -.. container:: justify - - Create a file named *input.lammps* - into the *Decorate/* folder, and copy - the following lines into it: - -.. code-block:: lammps - - units real - atom_style full - - read_data silica-deformed.data - displace_atoms all move -12 0 0 # optional - - pair_style reaxff NULL safezone 3.0 mincap 150 - pair_coeff * * ../RelaxSilica/reaxCHOFe.ff Si O H - fix myqeq all qeq/reaxff 1 0.0 10.0 1.0e-6 reaxff maxiter 400 - -.. container:: justify - - Here, the *displace_atoms* command was used to move the center of the crack - near the center of the box. This step is optional but makes the visualization - of the interface in VMD easier. A different value for the shift may be - needed in your case, depending on the location of the crack. - -.. container:: justify - - A difference with the previous input is that three atom types are specified - in the *pair_coeff* command, *Si O H*, instead of two. - -.. container:: justify - - Then, let us adapt some familiar commands to measure the charges of all - three types of atoms, and output the charge values into log files: - -.. code-block:: lammps - - group grpSi type 1 - group grpO type 2 - group grpH type 3 - variable qSi equal charge(grpSi)/count(grpSi) - variable qO equal charge(grpO)/count(grpO) - variable qH equal charge(grpH)/(count(grpH)+1e-10) - - thermo 5 - thermo_style custom step temp etotal press vol & - v_qSi v_qO v_qH - fix myspec all reaxff/species 5 1 5 species.log element Si O H - -.. container:: justify - - Here, the :math:`+1\text{e}-10` was added to the denominator of the - *variable qH* in order to avoid dividing by 0 at the beginning of the - simulation. - -.. container:: justify - - Finally, let us create a loop with 10 steps, and create two hydrogen atoms - at random locations at every step: - -.. code-block:: lammps - - fix mynvt all nvt temp 300.0 300.0 100 - timestep 0.5 - - label loop - variable a loop 10 - - variable seed equal 35672+${a} - create_atoms 3 random 2 ${seed} NULL overlap 2.6 maxtry 50 - group grpH type 3 - run 2000 - write_dump all custom dump.${a}.lammpstrj id type q x y z - - next a - jump SELF loop - - write_data decorated.data - -.. container:: justify - - Here, a different *lammpstrj* file is created for each step of the loop to - avoid creating dump files with varying numbers of atoms, which VMD can't - read. - -.. container:: justify - - Once the simulation is over, it can be seen from the *species.log* file that - all the created hydrogen atoms reacted with the :math:`\text{SiO}_{2}` - structure to form surface groups (such as hydroxyl (-OH) groups). - -.. code-block:: lammps - - # Timestep No_Moles No_Specs Si192O384 H - 5 3 2 1 2 - (...) - # Timestep No_Moles No_Specs Si192O384H20 - 20000 1 1 1 - -.. figure:: ../figures/level3/reactive-silicon-dioxide/decorated-light.png - :alt: Cracked silicon oxide after addition of hydrogen atoms - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/decorated-dark.png - :alt: Cracked silicon oxide after addition of hydrogen atoms - :class: only-dark - -.. container:: figurelegend - - Figure: Cracked silicon oxide after the addition of hydrogen atoms. Some - hydroxyl groups can be seen at the interfaces. The atoms are colored by - their charges. Bulk Si atoms - with a charge of about :math:`1.8~\text{e}` appear in red/orange, - and bulk O atoms with a charge of about :math:`-0.9 ~ \text{e}` appear in blue. - -.. include:: ../../non-tutorials/accessfile.rst - -Going further with exercises -============================ - -.. include:: ../../non-tutorials/link-to-solutions.rst - -Hydrate the structure ---------------------- - -.. container:: justify - - Add water molecules to the current structure, and follow the reactions over - time. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/hydrated-light.png - :alt: Cracked silicon oxide after addition of water molecule - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/hydrated-dark.png - :alt: Cracked silicon oxide after addition of water molecule - :class: only-dark - -.. container:: figurelegend - - Figure: Cracked silicon oxide after the addition of water - molecules. The atoms are colored by their charges. - -A slightly acidic bulk solution -------------------------------- - -.. container:: justify - - Create a bulk water system with a few hydronium ions (:math:`H_3O^+` - or :math:`H^+`) using ReaxFF. The addition of hydronium ions will make the - system acidic. - -.. figure:: ../figures/level3/reactive-silicon-dioxide/acidic-water-light.png - :alt: Acidic bulk water with ReaxFF - :class: only-light - -.. figure:: ../figures/level3/reactive-silicon-dioxide/acidic-water-dark.png - :alt: Acidic bulk water with ReaxFF - :class: only-dark - -.. container:: figurelegend - - Figure: Slightly acidic bulk water simulated with ReaxFF. The atoms are - colored by their charges. \ No newline at end of file diff --git a/docs/sphinx/source/tutorials/level3/water-adsorption-in-silica.rst b/docs/sphinx/source/tutorials/level3/water-adsorption-in-silica.rst deleted file mode 100644 index 2aa2b92d5..000000000 --- a/docs/sphinx/source/tutorials/level3/water-adsorption-in-silica.rst +++ /dev/null @@ -1,922 +0,0 @@ -.. _gcmc-silica-label: - -Water adsorption in silica -************************** - -.. container:: hatnote - - Dealing with a varying number of molecules - -.. figure:: ../figures/level3/water-adsorption-in-silica/avatar-dark.webp - :height: 250 - :alt: Water molecules adsorbed in silica SiO2 porous inorganic material - :class: only-dark - :align: right - -.. figure:: ../figures/level3/water-adsorption-in-silica/avatar-light.webp - :height: 250 - :alt: Water molecules adsorbed in silica SiO2 porous inorganic material - :class: only-light - :align: right - -.. container:: justify - - The objective of this tutorial is to combine molecular - dynamics and grand canonical Monte Carlo simulations to - compute the adsorption of water molecules in cracked silica material. - -.. container:: justify - - This tutorial illustrates the use of the grand canonical - ensemble in molecular simulation, an open ensemble in which - the number of atoms or molecules within the simulation box is not constant. - When using the grand canonical ensemble, it is possible to impose - the chemical potential (or pressure) of a given fluid - in a nanoporous structure. - -.. include:: ../../non-tutorials/recommand-lj.rst - -.. include:: ../../non-tutorials/cite.rst - -.. include:: ../../non-tutorials/2Aug2023.rst - -Generation of the silica block -============================== - -.. container:: justify - - Let us first generate a block of amorphous silica (SiO2). To do - so, we are going to replicate a building block containing 3 - Si and 6 O atoms. - -.. admonition:: Not interested in the annealing procedure ? - :class: info - - You can skip this part by downloading the final silica structure - |download_silica_block| and continue with the tutorial. - -.. |download_silica_block| raw:: html - - here - -.. container:: justify - - Create two folders side by side, and name them respectively *Potential/* - and *SilicaBlock/*. - -.. container:: justify - - An initial data file for the SiO atoms can be - downloaded by clicking |download_SiO.data|. - Save it in *SilicaBlock/*. This data file - contains the coordinates of the 9 atoms, their masses, and - their charges. The *.data* file can be directly read by LAMMPS using the - *read_data* command. Let us replicate these atoms using - LAMMPS, and apply an annealing procedure to obtain a block - of amorphous silica. - -.. admonition:: About annealing procedure - :class: info - - The annealing procedure consists of adjusting the system temperature in successive steps. - Here, a large initial temperature is chosen to ensure the melting of the SiO2 structure. - Then, several steps are used to progressively cool down the system until it solidifies and forms - amorphous silica. Depending on the material, different cooling velocities can sometimes - lead to different crystal structures or different degrees of defect. - -.. container:: justify - - Create a new input file named *input.lammps* in the *SilicaBlock/* folder, and copy - the following lines into it: - -.. |download_SiO.data| raw:: html - - here - -.. code-block:: lammps - - units metal - boundary p p p - atom_style full - pair_style vashishta - neighbor 1.0 bin - neigh_modify delay 1 - -.. container:: justify - - The main difference with some of the previous tutorials is the use of - the *Vashishta* pair style. Download the *Vashishta* potential by - clicking |download_vashishta|, - and copy it within the *Potential/* folder. - -.. |download_vashishta| raw:: html - - here - -.. admonition:: About the Vashishta potential - :class: info - - The |website_vashishta| - potential is a bond-angle energy-based potential, it - deduces the bonds between atoms from their relative - positions :cite:`vashishta1990interaction`. Therefore, there is no need to - provide the bond and angle information as we do with classic force fields - like GROMOS or AMBER. When used with LAMMPS, the *Vashishta* - potential requires the use of the *metal* units system. - Bond-angle energy-based potentials - are more computationally heavy than classical force - fields and require the use of a smaller timestep, but - they allow for the modeling of bond formation and - breaking, which is what we need here as we want to create - a crack in the silica. - -.. |website_vashishta| raw:: html - - Vashishta - -.. container:: justify - - Let us then import the system made of 9 atoms, and replicate it four times in all three - directions of space, thus creating a system with 576 atoms. Add the following lines - to *input.lammps*: - -.. code-block:: lammps - - read_data SiO.data - replicate 4 4 4 - -.. container:: justify - - Then, let us specify the pair coefficients by indicating - that the first atom type is *Si*, and - the second is *O*. Let us also - add a dump command for printing out the positions of the - atoms every 5000 steps: - -.. code-block:: lammps - - pair_coeff * * ../Potential/SiO.1990.vashishta Si O - -.. container:: justify - - Let us add some commands to *input.lammps* to help us follow the evolution of the system, - such as its temperature, volume, and potential energy: - -.. code-block:: lammps - - dump dmp all atom 5000 dump.lammpstrj - variable myvol equal vol - variable mylx equal lx - variable myly equal ly - variable mylz equal lz - variable mypot equal pe - variable mytemp equal temp - fix myat1 all ave/time 10 100 1000 v_mytemp file temperature.dat - fix myat2 all ave/time 10 100 1000 & - v_myvol v_mylx v_myly v_mylz file dimensions.dat - fix myat3 all ave/time 10 100 1000 v_mypot file potential-energy.dat - thermo 1000 - -.. container:: justify - - Finally, let us create the last part of our script. The - annealing procedure is made of four consecutive runs. - First, a :math:`50\,\text{ps}` - phase at :math:`T = 6000\,\text{K}` - and isotropic pressure coupling with desired pressure :math:`p = 100\,\text{atm}`: - -.. code-block:: lammps - - velocity all create 6000 4928459 rot yes dist gaussian - fix npt1 all npt temp 6000 6000 0.1 iso 100 100 1 - timestep 0.001 - run 50000 - -.. container:: justify - - Then, a second phase during which the system is cooled down - from :math:`T = 6000\,\text{K}` - to :math:`T = 4000\,\text{K}`. - An anisotropic pressure coupling is used, allowing all three - dimensions of the box to evolve independently from one another: - -.. code-block:: lammps - - fix npt1 all npt temp 6000 4000 0.1 aniso 100 100 1 - run 50000 - -.. container:: justify - - Then, let us cool down the system - further while also reducing the pressure, then perform a - small equilibration step at the final desired condition, :math:`T = 300\,\text{K}` - and :math:`p = 1\,\text{atm}`. - -.. code-block:: lammps - - fix npt1 all npt temp 4000 300 0.1 aniso 100 1 1 - run 200000 - fix npt1 all npt temp 300 300 0.1 aniso 1 1 1 - run 50000 - - write_data amorphousSiO.data - -.. container:: justify - - *Disclaimer --* I created this procedure by intuition and - not from proper calibration, do not copy it without - making your tests if you intend to publish your results. - -.. admonition:: Anisotropic versus isotropic barostat - :class: info - - Here, an isotropic barostat is used for the melted phase at :math:`T = 6000\,\text{K}`, - and then an anisotropic barostat is used for all following phases. With the anisotropic - barostat, all three directions of space are adjusted independently from one another. Such - anisotropic barostat is usually a better choice for a solid phase. For a - liquid or a gas, the isotropic barostat is usually the best choice. - -.. container:: justify - - The simulation takes about 15-20 minutes on 4 CPU cores. - -.. container:: justify - - Let us check the evolution of the temperature from the *temperature.dat* file. - Apart from an initial spike (which may be due to an initial - bad configuration, probably harmless here), - the temperature follows well the desired annealing procedure. - -.. figure:: ../figures/level3/water-adsorption-in-silica/temperature_evolution-dm.png - :alt: silica temperature during annealing, from melt to solid - :class: only-dark - -.. figure:: ../figures/level3/water-adsorption-in-silica/temperature_evolution.png - :alt: silica temperature during annealing, from melt to solid - :class: only-light - -.. container:: figurelegend - - Figure: Temperature of the system during annealing. The vertical dashed lines - mark the transition between the different phases of the simulations. - -.. container:: justify - - Let us also make sure that the box was indeed deformed isotropically during the first - stage of the simulation, and then anisotropically by plotting the evolution of the - box dimensions over time. - -.. figure:: ../figures/level3/water-adsorption-in-silica/dimensions_evolution-dm.png - :alt: box dimensions during annealing, from melt to solid - :class: only-dark - -.. figure:: ../figures/level3/water-adsorption-in-silica/dimensions_evolution.png - :alt: box dimensions during annealing, from melt to solid - :class: only-light - -.. container:: figurelegend - - Figure: Box dimensions during annealing. The vertical dashed lines - mark the transition between the different phases of the simulations. - -.. figure:: ../figures/level3/water-adsorption-in-silica/generated-silica-dark.png - :alt: silica block generated by temperature annealing using LAMMPS - :class: only-dark - -.. figure:: ../figures/level3/water-adsorption-in-silica/generated-silica-light.png - :alt: silica block generated by temperature annealing using LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: Snapshot of the final amorphous silica (SiO2) with Si atom in yellow and - O atoms in red. - -.. container:: justify - - After running the simulation, the final LAMMPS topology file named - *amorphousSiO.data* will be located in *SilicaBlock/*. - -.. admonition:: Tip for research project - :class: info - - In the case of a research project, the validity of the generated - structure must be tested and compared to reference values, ideally from - experiments. For instance, radial distribution functions or Young modulus - can both be compared to experimental values. This is beyond the - scope of this tutorial. - -Cracking the silica -=================== - -.. container:: justify - - Let us dilate the block of silica until a - crack forms. Create a new folder called *Cracking/* next to *SilicaBlock/*, - as well as a new *input.lammps* file starting with familiar lines as - previously: - -.. code-block:: lammps - - units metal - boundary p p p - atom_style full - neighbor 1.0 bin - neigh_modify delay 1 - - read_data ../SilicaBlock/amorphousSiO.data - - pair_style vashishta - pair_coeff * * ../Potential/SiO.1990.vashishta Si O - dump dmp all atom 1000 dump.lammpstrj - -.. container:: justify - - Let us progressively increase the size of the - box in the :math:`x` direction, thus forcing the silica to deform - and eventually crack. To do - so, a loop based on the jump command is used. At - every step of the loop, the box dimension over :math:`x` will - be multiplied by a scaling factor 1.005. Add the following lines into - the *input.lammps*: - -.. code-block:: lammps - - fix nvt1 all nvt temp 300 300 0.1 - timestep 0.001 - thermo 1000 - variable var loop 45 - label loop - change_box all x scale 1.005 remap - run 2000 - next var - jump input.lammps loop - run 20000 - write_data dilatedSiO.data - -.. container:: justify - - The *fix nvt* is used to control the temperature of the system, while the *change_box* command - imposes incremental deformations of the box. - Different scaling factors or/and different numbers of - steps can be used to generate different defects in the silica. - -.. admonition:: On using barostat during deformation - :class: info - - .. container:: justify - - Here, box deformations are applied in the x direction, while the - y and z box dimensions are kept constants. - - .. container:: justify - - Another possible choice is to apply a barostat along the y and z - directions, allowing for the system to adjust to the stress. In LAMMPS, - this can be done by using : - - .. code-block:: lammps - - fix npt1 all npt temp 300 300 0.1 y 1 1 1 z 1 1 1 - - .. container:: justify - - instead of: - - .. code-block:: lammps - - fix nvt1 all nvt temp 300 300 0.1 - -.. figure:: ../figures/level3/water-adsorption-in-silica/cracked-dark.png - :alt: silica block with crack - :class: only-dark - -.. figure:: ../figures/level3/water-adsorption-in-silica/cracked-light.png - :alt: silica block with crack - :class: only-light - -.. container:: figurelegend - - Figure: Block of silica after deformation with Si atom in yellow and O - atoms in red. Some holes are visible - -.. container:: justify - - After the expansion, a final equilibration step of a duration of 20 - picoseconds is performed. If you look at the *dump.lammpstrj* file - using VMD, you can see the expansion occurring step-by-step, and the - atoms progressively adjusting to the box dimensions. - -.. container:: justify - - At first, the deformations - are reversible (elastic regime). At some point, bonds - start breaking and dislocations appear (plastic regime). - -.. container:: justify - - Alternatively, you can download the final state directly by clicking - |download_silica_dilated|. - -.. |download_silica_dilated| raw:: html - - here - -.. admonition:: Passivated silica - :class: info - - In ambient conditions, some of the surface SiO2 atoms are chemically - passivated by forming covalent bonds with hydrogen (H) - atoms. For the sake of simplicity, we are not going to - add surface hydrogen atoms here. An example of a procedure allowing - for properly inserting hydrogen atoms is used - in :ref:`reactive-silicon-dioxide-label`. - -Adding water -============ - -.. container:: justify - - In order to add the water molecules to the silica, we are - going to use the Monte Carlo method in the grand canonical - ensemble (GCMC). In short, the system is put into contact - with a virtual reservoir of a given chemical potential - :math:`\mu`, and multiple attempts to insert water - molecules at random positions are made. Each attempt is - either accepted or rejected based on energy considerations. Find more details - in classical textbooks :cite:`frenkel2023understanding`. - -Using hydrid potentials ------------------------ - -.. container:: justify - - Create a new folder called *Addingwater/*. Download and save the - |download_TIP4P2005| file for the - water molecule within *Addingwater/*. - -.. |download_TIP4P2005| raw:: html - - template - -.. container:: justify - - Create a new input file called *input.lammps* - within *Addingwater/*, and copy the - following lines into it: - -.. code-block:: lammps - - units metal - boundary p p p - atom_style full - neighbor 1.0 bin - neigh_modify delay 1 - pair_style hybrid/overlay vashishta lj/cut/tip4p/long 3 4 1 1 0.1546 10 - kspace_style pppm/tip4p 1.0e-4 - bond_style harmonic - angle_style harmonic - -.. container:: justify - - There are several differences with the previous input files - used in this tutorial. From now on, the system will combine water and silica, - and therefore two force fields are combined: Vashishta for - SiO, and lj/cut/tip4p/long for TIP4P water model (here - the TIP4P/2005 model is used :cite:`abascal2005general`). - Combining the two force fields is done using the *hybrid/overlay* pair style. - -.. admonition:: About hybrid and hybrid/overlay pair style - :class: info - - From the LAMMPS documentation: - The hybrid and hybrid/overlay styles enable the use - of multiple pair styles in one simulation. With the hybrid style, - exactly one pair style is assigned to each pair of atom types. - With the hybrid/overlay and hybrid/scaled styles, one or more pair - styles can be assigned to each pair of atom types. - -.. container:: justify - - The *kspace* solver is used to calculate the long - range Coulomb interactions associated with *tip4p/long*. - Finally, the style for the bonds and angles - of the water molecules are defined, although they are not important - since it is a rigid water model. - -.. container:: justify - - Before going further, we also need to make a few changes to our data file. - Currently, *dilatedSiO.data* only includes two atom types, but - we need four. Copy the previously generated *dilatedSiO.data* - file within *Addingwater/*. Currently, *dilatedSiO.data* starts with: - -.. code-block:: lammps - - 576 atoms - 2 atom types - - -5.512084438507452 26.09766215010596 xlo xhi - -0.12771230207837192 20.71329001367807 ylo yhi - 3.211752393088563 17.373825318513106 zlo zhi - - Masses - - 1 28.0855 - 2 15.9994 - - Atoms # full - - (...) - -.. container:: justify - - Make the following changes to allow for the addition of water - molecules. Modify the file so that it looks like the following - (with 4 atom types, 1 bond type, 1 angle type, and four masses): - -.. code-block:: lammps - - 576 atoms - 4 atom types - 1 bond types - 1 angle types - - 2 extra bond per atom - 1 extra angle per atom - 2 extra special per atom - - 0.910777522101565 19.67480018949893 xlo xhi - 2.1092682236518137 18.476309487947546 ylo yhi - -4.1701120819606885 24.75568979356097 zlo zhi - - Masses - - 1 28.0855 - 2 15.9994 - 3 15.9994 - 4 1.008 - - Atoms # full - - (...) - -.. container:: justify - - Doing so, we anticipate that there will be 4 atom types in - the simulations, with O and H of H2O having indexes 3 and 4, - respectively. There will also be 1 bond type and 1 angle - type. The extra bond, extra angle, and extra special lines - are here for memory allocation. - -.. container:: justify - - We can continue to fill in the - *input.lammps* file, by adding the system definition: - -.. code-block:: lammps - - read_data dilatedSiO.data - molecule h2omol H2O.mol - lattice sc 3 - create_atoms 0 box mol h2omol 45585 - lattice none 1 - - group SiO type 1 2 - group H2O type 3 4 - -.. container:: justify - - After reading the data file and defining the h2omol molecule - from the *.txt* file, the *create_atoms* command is used to - include some water molecules in the system on a - simple cubic lattice. Not adding a molecule before starting the - GCMC steps usually lead to failure. Note that here, - most water molecules overlap with the silica. These - overlapping water molecules will be deleted before - starting the simulation. - -.. container:: justify - - Then, add the following settings to *input.lammps*: - -.. code-block:: lammps - - pair_coeff * * vashishta ../Potential/SiO.1990.vashishta Si O NULL NULL - pair_coeff * * lj/cut/tip4p/long 0 0 - # epsilonSi = 0.00403, sigmaSi = 3.69 - # epsilonO = 0.0023, sigmaO = 3.091 - pair_coeff 1 3 lj/cut/tip4p/long 0.0057 4.42 - pair_coeff 2 3 lj/cut/tip4p/long 0.0043 3.12 - pair_coeff 3 3 lj/cut/tip4p/long 0.008 3.1589 - pair_coeff 4 4 lj/cut/tip4p/long 0.0 0.0 - bond_coeff 1 0 0.9572 - angle_coeff 1 0 104.52 - - variable oxygen atom "type==3" - group oxygen dynamic all var oxygen - variable nO equal count(oxygen) - fix myat1 all ave/time 100 10 1000 v_nO file numbermolecule.dat - - fix shak H2O shake 1.0e-4 200 0 b 1 a 1 mol h2omol - -.. container:: justify - - The force field Vashishta applies only to Si (type 1) - and O of SiO2 (type 2), - and not to the O and H of H2O, thanks to the NULL - parameters used for atoms of types 3 and 4. - -.. container:: justify - - Pair coefficients for lj/cut/tip4p/long are - defined between O atoms, as well as between - O(SiO)-O(H2O) and Si(SiO)-O(H2O). Therefore, the fluid-solid - interactions will be set by Lennard-Jones and Coulomb potentials. - -.. container:: justify - - The number of oxygen atoms from water molecules (i.e. the number of molecules) - will be printed in the file *numbermolecule.dat*. - -.. container:: justify - - The SHAKE algorithm is used to - maintain the shape of the water molecules over time. Some of - these features have been seen in previous tutorials. - -.. container:: justify - - Let us delete the overlapping water molecules, and print the - positions of the remaining atoms in a *.lammpstrj* file by adding the following - lines into *input.lammps*: - -.. code-block:: lammps - - delete_atoms overlap 2 H2O SiO mol yes - dump dmp all atom 1000 dump.init.lammpstrj - -GCMC simulation ---------------- - -.. container:: justify - - To prepare for the GCMC simulation, - let us make the first equilibration step - by adding the following lines into *input.lammps*: - -.. code-block:: lammps - - compute_modify thermo_temp dynamic yes - compute ctH2O H2O temp - compute_modify ctH2O dynamic yes - fix mynvt1 H2O nvt temp 300 300 0.1 - fix_modify mynvt1 temp ctH2O - compute ctSiO SiO temp - fix mynvt2 SiO nvt temp 300 300 0.1 - fix_modify mynvt2 temp ctSiO - timestep 0.001 - thermo 1000 - run 5000 - -.. admonition:: On thermostating groups instead of the entire system - :class: info - - Two different thermostats are used for SiO and H2O, respectively. Using - separate thermostats is usually better when the system contains two separate - species, such as a solid and a liquid. It is particularly important to use two thermostats - here because the number of water molecules will fluctuate with time. - -.. container:: justify - - The *compute_modify* with - *dynamic yes* for water is used to specify that the - number of molecules is not constant. - -.. container:: justify - - Finally, let us use the *fix gcmc* and perform the grand - canonical Monte Carlo steps. Add the following lines into *input.lammps*: - -.. code-block:: lammps - - variable tfac equal 5.0/3.0 - variable xlo equal xlo+0.1 - variable xhi equal xhi-0.1 - variable ylo equal ylo+0.1 - variable yhi equal yhi-0.1 - variable zlo equal zlo+0.1 - variable zhi equal zhi-0.1 - region system block ${xlo} ${xhi} ${ylo} ${yhi} ${zlo} ${zhi} - fix fgcmc H2O gcmc 100 100 0 0 65899 300 -0.5 0.1 & - mol h2omol tfac_insert ${tfac} group H2O shake shak & - full_energy pressure 10000 region system - run 45000 - write_data SiOwithwater.data - write_dump all atom dump.lammpstrj - -.. admonition:: Dirty fix - :class: info - - The region *system* was created to avoid the error *Fix gcmc - region extends outside simulation box* - which seems to occur with the 2Aug2023 LAMMPS version. - -.. container:: justify - - The *tfac_insert* option ensures that the correct estimate is - made for the temperature of the inserted water molecules by - taking into account the internal degrees of freedom. Running - this simulation, you should see the number of molecules - increasing progressively. When using the pressure argument, - LAMMPS ignores the value of the chemical potential [here :math:`\mu = -0.5\,\text{eV}`, - which corresponds roughly to ambient conditions (i.e. :math:`\text{RH} \approx 50\,\%`) - :cite:`gravelle2020multi`.] The large pressure value of 10000 bars was chosen to ensure that - some successful insertions of molecules would occur during the - extremely short duration of this simulation. - -.. container:: justify - - When you run the simulation, make sure that some water molecules - remain in the system after the *delete_atoms* command. You can control - that either using the log file or using the *numbermolecule.dat* data file. - -.. container:: justify - - You can see, by looking at the log file, that 280 molecules - were added by the *create_atoms* command (the exact number you get may differ): - -.. code-block:: bw - - Created 840 atoms - -.. container:: justify - - You can also see that 258 molecules were immediately deleted, - leaving 24 water molecules (the exact number you get may differ): - -.. code-block:: bw - - Deleted 774 atoms, new total = 642 - Deleted 516 bonds, new total = 44 - Deleted 258 angles, new total = 22 - -.. container:: justify - - After just a few GCMC steps, - the number of molecules starts increasing. - Once the crack is filled with water molecules, the number of - molecules reaches a plateau. - -.. figure:: ../figures/level3/water-adsorption-in-silica/number_evolution-dm.png - :alt: number of water molecules added by the LAMMPS gcmc - :class: only-dark - -.. figure:: ../figures/level3/water-adsorption-in-silica/number_evolution.png - :alt: number of water molecules added by the LAMMPS gcmc - :class: only-light - -.. container:: figurelegend - - Figure: Number of molecules in the system as a function of the time :math:`t`. - The dashed vertical line marks the beginning of the GCMC step. - -.. container:: justify - - The final number of molecules depends on the imposed pressure, - temperature, and on the interaction between water and silica (i.e. its hydrophilicity). - -.. figure:: ../figures/level3/water-adsorption-in-silica/solvated-dark.png - :alt: silica block with water and crack - :class: only-dark - -.. figure:: ../figures/level3/water-adsorption-in-silica/solvated-light.png - :alt: silica block with water and crack - :class: only-light - -.. container:: figurelegend - - Figure: Snapshot of the silica system after the adsorption of the water molecules, - with the oxygen of the water molecules represented in cyan. - -.. container:: justify - - Note that GCMC simulations of such dense phases are usually slow to converge due to the - very low probability of successfully inserting a molecule. Here, the short simulation - duration was made possible by the use of a large pressure. - -.. admonition:: Vizualising varying number of molecules - :class: info - - By default, VMD fails to properly render systems with varying numbers of atoms. - -.. include:: ../../non-tutorials/accessfile.rst - -Going further with exercises -============================ - -.. include:: ../../non-tutorials/link-to-solutions.rst - -Mixture adsorption ------------------- - -.. container:: justify - - Adapt the existing script and insert both :math:`\text{CO}_2` molecules - and water molecules within the silica crack using GCMC. - Download the |CO2-template|. The parameters for the - :math:`\text{CO}_2` - molecule are the following: - -.. code-block:: lammps - - pair_coeff 5 5 lj/cut/tip4p/long 0.0179 2.625854 - pair_coeff 6 6 lj/cut/tip4p/long 0.0106 2.8114421 - bond_coeff 2 46.121 1.17 - angle_coeff 2 2.0918 180 - -.. container:: justify - - The atom of type 5 is an oxygen of - mass 15.9994, and the atom of type 6 is a carbon of mass 12.011. - -.. figure:: ../figures/level3/water-adsorption-in-silica/H2O-CO2-dark.png - :alt: silica block adsorbed water and CO2 - :class: only-dark - -.. figure:: ../figures/level3/water-adsorption-in-silica/H2O-CO2-light.png - :alt: silica block adsorbed water and CO2 - :class: only-light - -.. container:: figurelegend - - Figure: Cracked silica with adsorbed water and :math:`\text{CO}_2` molecules (in green). - -.. |CO2-template| raw:: html - - CO2 template - -Adsorb water in ZIF-8 nanopores -------------------------------- - -.. figure:: ../figures/level3/water-adsorption-in-silica/zif8-dark.png - :alt: zif-8 with water - :class: only-dark - :height: 250 - :align: right - -.. figure:: ../figures/level3/water-adsorption-in-silica/zif8-light.png - :alt: zif-8 with water - :class: only-light - :height: 250 - :align: right - -.. container:: justify - - Use the same protocol as the one implemented in this tutorial to add water - molecules to a Zif-8 nanoporous material. A snapshot of the system with a - few water molecules is shown on the right. - -.. container:: justify - - Download the initial Zif-8 |Zif-8-structure|, - the |Zif-8-parameters| file, and this - new |water-template|. The ZIF-8 structure is made - of 7 atom types (C1, C2, C3, H2, H3, N, Zn), connected - by bonds, angles, dihedrals, and impropers. It uses the - same *pair_style* as water, - so there is no need to use *hybrid pair_style*. - Your *input* file should start like this: - -.. code-block:: lammps - - units real - atom_style full - boundary p p p - bond_style harmonic - angle_style harmonic - dihedral_style charmm - improper_style harmonic - - pair_style lj/cut/tip4p/long 1 2 1 1 0.105 14.0 - kspace_style pppm/tip4p 1.0e-5 - - special_bonds lj 0.0 0.0 0.5 coul 0.0 0.0 0.833 - -.. container:: justify - - An important note: here, water occupies the atom types 1 and 2, - instead of 3 and 4 in the case of SiO2 from the main section - of the tutorial. - -.. |Zif-8-structure| raw:: html - - structure - -.. |Zif-8-parameters| raw:: html - - parameters - -.. |water-template| raw:: html - - water template \ No newline at end of file diff --git a/docs/sphinx/source/tutorials/mdanalysis/mdanalysis-tutorial.rst b/docs/sphinx/source/tutorials/mdanalysis/mdanalysis-tutorial.rst deleted file mode 100644 index 5e19db28e..000000000 --- a/docs/sphinx/source/tutorials/mdanalysis/mdanalysis-tutorial.rst +++ /dev/null @@ -1,473 +0,0 @@ -.. _mda-label: - -MDAnalysis tutorials -******************** - -.. container:: hatnote - - Perform post-mortem analysis using Python and MDAnalysis - -.. container:: justify - - There are two main ways to analyze data from a molecular dynamics simulation: - (1) on-the-fly analysis, for instance using *fix ave/time* command, - and (2) post-mortem analysis, using the trajectories dumped in the *lammpstrj* file. - -.. container:: justify - - The main advantage of post-mortem analysis is that there is no need to - know what we want to measure before starting the simulation. - -.. container:: justify - - In these short tutorials, several trajectories are - imported into Python using MDAnalysis and different - information is extracted. All the trajectories required for these - tutorials are provided below and were generated from one of the LAMMPS tutorials. - -.. include:: ../../non-tutorials/needhelp.rst - -.. figure:: ../figures/level2/polymer-in-water/PEG-dark.webp - :alt: Movie of a peg molecule in water as simulated with LAMMPS - :height: 250 - :align: right - :class: only-dark - -.. figure:: ../figures/level2/polymer-in-water/PEG-light.webp - :alt: Movie of a peg molecule in water as simulated with LAMMPS - :height: 250 - :align: right - :class: only-light - -Simple trajectory import -======================== - -.. container:: justify - - Here, we re-use a trajectory generated - during the :ref:`all-atoms-label` tutorial. - Download the |dump_all_atom| - and the |data_all_atom| files - to continue with this tutorial. - -.. |dump_all_atom| raw:: html - - dump - -.. |data_all_atom| raw:: html - - data - -Create a Universe ------------------ - -.. container:: justify - - Open a new Jupyter notebook and - call it *simple_import.ipynb*. First, let us import both *MDAnalysis* - and *NumPy* by copying the following lines into *simple_import.ipynb*. - -.. code-block:: python - - import MDAnalysis as mda - import numpy as np - -.. container:: justify - - Then, let us create a MDAnalysis *universe* using - the LAMMPS data file *mix.data* as topology, - and the *dump.lammpstrj* file as trajectory. - Add the following lines into the notebook - (adapt the *path_to_data* accordingly): - -.. code-block:: python - - path_to_data = "./" - u = mda.Universe(path_to_data + "mix.data", - path_to_data + "dump.lammpstrj", - topology_format="data", format="lammpsdump") - -Read topology information -------------------------- - -.. container:: justify - - From the :ref:`all-atoms-label` tutorial, we know that atom - types 1 to 7 are from the PEG atoms, and atom types 8 and 9 are from - the water molecules. - -.. container:: justify - - One can create atom groups using the atom types - with the *select_atoms* option of MDAnalysis: - -.. code-block:: python - - peg = u.select_atoms("type 1 2 3 4 5 6 7") - h2o = u.select_atoms("type 8 9") - -.. container:: justify - - Let us print the number of atoms in each group: - -.. code-block:: python - - print("atoms in peg:", peg.atoms.n_atoms) - print("atoms in h2o:", h2o.atoms.n_atoms) - -.. code-block:: bw - - atoms in peg: 101 - atoms in h2o: 3045 - -.. container:: justify - - Atom groups are atom containers, from which - information about the atoms can be read. - For instance, one can loop over the 6 first atoms - from the peg group, and extract their IDs, - types, masses, and charges: - -.. code-block:: python - - for atom in peg[:6]: - id = atom.id - type = atom.type - mass = atom.mass - charge = np.round(atom.charge,2) - print("Atom id:", id, "type:", type, "mass:", mass, "g/mol charge:", charge, "e") - - Atom id: 3151 type: 4 mass: 1.008 g/mol charge: 0.19 e - Atom id: 3152 type: 6 mass: 15.9994 g/mol charge: -0.31 e - Atom id: 3153 type: 5 mass: 12.011 g/mol charge: 0.06 e - Atom id: 3154 type: 3 mass: 1.008 g/mol charge: 0.05 e - Atom id: 3155 type: 3 mass: 1.008 g/mol charge: 0.05 e - Atom id: 3156 type: 2 mass: 12.011 g/mol charge: 0.02 e - -Extract temporal evolution --------------------------- - -.. container:: justify - - Let us extract the position of the first atom - of the peg group (i.e. the hydrogen of type 4), - and store its coordinates in each frame into a list: - -.. code-block:: python - - atom1 = peg[0] - position_vs_time = [] - for ts in u.trajectory: - x, y, z = atom1.position - position_vs_time.append([ts.frame, x, y, z]) - -.. container:: justify - - Here, the for loop runs over all the frames, and the x, y, and z coordinates - of the atom named *atom1* are read. Here *ts.frame* is the id of the frame, - it goes from 0 to 300, i.e. the total number of frames. The *position_vs_time* list - contains 301 items, each item being the frame id, and the corresponding coordinates of *atom1*. - -.. container:: justify - - One can use Matplotlib Pyplot to visualize all the x and y coordinates occupied by *atom1* - during the simulation. - -.. figure:: ../figures/mdanalysis/mdanalysis-tutorial/position-atom-dark.png - :alt: plot of the position-atom - :class: only-dark - -.. figure:: ../figures/mdanalysis/mdanalysis-tutorial/position-atom-light.png - :alt: plot of the position-atom - :class: only-light - -.. container:: figurelegend - - Figure: Position of the *atom1* along time. The size of the disks - is proportional to the frame ID. - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/CNT_dark.webp - :alt: carbon nanotube image in vacuum - :height: 250 - :align: right - :class: only-dark - -.. figure:: ../figures/level1/breaking-a-carbon-nanotube/CNT_light.webp - :alt: carbon nanotube image in vacuum - :height: 250 - :align: right - :class: only-light - -Counting the bonds of a CNT -=========================== - -.. container:: justify - - Here, we re-use the trajectory generated - during the second part *Breakable bonds* - of the :ref:`carbon-nanotube-label` tutorial. - It is recommended that you follow this tutorial - first, but you can also directly download the |dump_cnt| - file and the |data_cnt| file and continue with this MDA tutorial. - -.. |dump_cnt| raw:: html - - dump - -.. |data_cnt| raw:: html - - data - -Create a Universe ------------------ - -.. container:: justify - - Open a new Jupyter Notebook and - call it *measure_bond_evolution.ipynb*. First, - let us import both *MDAnalysis* - and *NumPy* by copying the following - lines into *measure_bond_evolution.ipynb*. - -.. code-block:: python - - import MDAnalysis as mda - import numpy as np - -.. container:: justify - - Then, let us create a MDAnalysis *universe* using - the LAMMPS data file *cnt_atom.data* as topology, - and the *lammpstrj* file as trajectory. - Add the following lines into *measure_bond_evolution.ipynb*: - -.. code-block:: python - - path_to_data = "./" - u = mda.Universe(path_to_data + "cnt_deformed.data", - path_to_data + "dump.lammpstrj", - topology_format="data", format="lammpsdump", - atom_style='id type xs ys zs', - guess_bonds=True, vdwradii={'1':1.7}) - -.. container:: justify - - Since the *.data* file does not contain any bond information - the original bonds are guessed using the bond guesser - of MDAnalysis using *guess_bonds=True*. - -.. container:: justify - - Note that the bond guesser of MDAnalysis will not update the list of bond - over time, so we will need to use a few tricks to extract the evolution - of the number of bonds with time. - -.. container:: justify - - Let us create a single-atom group - named *cnt* and containing all the carbon atoms, - i.e. all the atoms of type 1, - by adding the following lines into *measure_bond_evolution.ipynb*. - -.. code-block:: python - - cnt = u.select_atoms("type 1") - -Some basics of MDAnalysis -------------------------- - -.. container:: justify - - MDAnalysis allows us to easily access information concerning the simulation, such - as the number of atoms, or the number of frames in the trajectory: - -.. code-block:: python - - print("Number of atoms =", cnt.n_atoms) - print("Number of frames =", u.trajectory.n_frames) - - Number of atoms = 690 - Number of frames = 286 - -.. container:: justify - - It is also possible to access the indexes of the atoms that - are considered as bonded by the bond guesser of MDAnalysis: - -.. code-block:: python - - print(cnt.atoms.bonds.indices) - - [[ 0 2] - [ 0 23] - [ 0 56] - (...) - [686 687] - [686 689] - [688 689]] - -.. container:: justify - - MDAnalysis also offers the possibility to loop over all the frame of the trajectory using: - -.. code-block:: python - - for ts in u.trajectory: - print(ts.frame) - - 0 - 1 - 2 - 3 - (...) - 283 - 284 - 285 - -.. container:: justify - - The positions of the atoms can also be obtained using: - -.. code-block:: python - - u.atoms.positions - - array([[ 75.14728 , 78.17872 , 95.61408 ], - [ 75.33008 , 77.751114, 93.20232 ], - [ 75.550476, 77.34152 , 94.54224 ], - ..., - [ 84.66992 , 82.24888 , 143.84988 ], - [ 84.66992 , 82.24888 , 147.60156 ], - [ 84.85272 , 81.82128 , 146.26175 ]], dtype=float32) - -.. container:: justify - - where the three columns of the array are the *x*, - *y*, - and *z* coordinates of the atoms. - -Counting the bonds ------------------- - -.. container:: justify - - In order to measure the evolution of the number of - bonds over time, let us loop over the trajectory - and manually extract the inter-atomic distance over time. - -.. container:: justify - - To do so, for every step of the trajectory, let us - loop over the indexes of the atoms that were initially - detected as bonded, and calculate the - distance between the two atoms, which can be done using: - -.. code-block:: python - - for ts in u.trajectory: - for id1, id2 in cnt.atoms.bonds.indices: - # detect positions - pos1 = u.atoms.positions[u.atoms.indices == id1][0] - pos2 = u.atoms.positions[u.atoms.indices == id2][0] - r = np.sqrt(np.sum((pos1-pos2)**2)) - -.. container:: justify - - Then, let us assume that if :math:`r` is larger that a - certain cut-off value of, let's say, :math:`1.8\,Å`, - the bond is broken: - -.. code-block:: python - - for ts in u.trajectory: - for id1, id2 in cnt.atoms.bonds.indices: - pos1 = u.atoms.positions[u.atoms.indices == id1][0] - pos2 = u.atoms.positions[u.atoms.indices == id2][0] - r = np.sqrt(np.sum((pos1-pos2)**2)) - if r < 1.8: - print("the bond has a length", r, "Å") - else: - print("the bond is broken") - -.. container:: justify - - Finally, let us store both the mean length of the bonds - and the total number of bonds in lists. - -.. code-block:: python - - lbond_vs_frame = [] - nbond_vs_frame = [] - for ts in u.trajectory: - frame = ts.frame - all_bonds_ts = [] # temporary list to store bond length - for id1, id2 in cnt.atoms.bonds.indices: - pos1 = u.atoms.positions[u.atoms.indices == id1] - pos2 = u.atoms.positions[u.atoms.indices == id2] - r = np.sqrt(np.sum((pos1-pos2)**2)) - if r < 1.8: - all_bonds_ts.append(r) - mean_length_bonds = np.mean(all_bonds_ts) - number_of_bond = len(all_bonds_ts)/2 # divide by 2 to avoid counting twice - lbond_vs_frame.append([frame, mean_length_bonds]) - nbond_vs_frame.append([frame, number_of_bond]) - -.. container:: justify - - The data can then be saved to files: - -.. code-block:: python - - np.savetxt("number_bond_vs_time.dat", nbond_vs_frame) - np.savetxt("length_bond_vs_time.dat", lbond_vs_frame) - -.. figure:: ../figures/mdanalysis/mdanalysis-tutorial/bond-dark.png - :alt: plot of the bond length and distance versus time - :class: only-dark - -.. figure:: ../figures/mdanalysis/mdanalysis-tutorial/bond-light.png - :alt: plot of the bond length and distance versus time - :class: only-light - -.. container:: figurelegend - - Figure: Evolution of the average bond length (a) and bond number (b) as a function of time. - -Bond length distributions -------------------------- - -.. container:: justify - - Using a similar script, - let us extract the bond length distribution - at the beginning of the simulation (let us say the 20 first frame), - as well as near the maximum deformation of the CNT: - -.. code-block:: python - - bond_length_distributions = [] - for ts in u.trajectory: - all_bonds_ts = [] - for id1, id2 in cnt.atoms.bonds.indices: - pos1 = u.atoms.positions[u.atoms.indices == id1] - pos2 = u.atoms.positions[u.atoms.indices == id2] - r = np.sqrt(np.sum((pos1-pos2)**2)) - if r < 1.8: - all_bonds_ts.append(r) - if frame > 0: # ignore the first frame - histo, r_val = np.histogram(all_bonds_ts, bins=50, range=(1.3, 1.65)) - r_val = (r_val[1:]+r_val[:-1])/2 - bond_length_distributions.append(np.vstack([r_val, histo])) - -.. figure:: ../figures/mdanalysis/mdanalysis-tutorial/bond-distribution-dark.png - :alt: plot of the bond distribution - :class: only-dark - -.. figure:: ../figures/mdanalysis/mdanalysis-tutorial/bond-distribution-light.png - :alt: plot of the bond distribution - :class: only-light - -.. container:: figurelegend - - Figure: Distribution in bond length near the start of the simulation, - as well as near the maximum deformation of the CNT. \ No newline at end of file diff --git a/docs/sphinx/source/tutorials/vmd/vmd-tutorial.rst b/docs/sphinx/source/tutorials/vmd/vmd-tutorial.rst deleted file mode 100644 index f62d93a97..000000000 --- a/docs/sphinx/source/tutorials/vmd/vmd-tutorial.rst +++ /dev/null @@ -1,357 +0,0 @@ -.. _vmd-label: - -VMD tutorial -************ - -.. container:: hatnote - - Generate good-looking images and movies with VMD - -.. figure:: ../figures/vmd/vmd-tutorial/avatar-dark.png - :alt: Image of the lammps polymer-water system generated with VMD visual representation - :height: 250 - :align: right - :class: only-dark - -.. figure:: ../figures/vmd/vmd-tutorial/avatar-light.png - :alt: Image of the lammps polymer-water system generated with VMD visual representation - :height: 250 - :align: right - :class: only-light - -.. container:: justify - - Visual Molecular Dynamics (VMD) is a free molecular graphics software - that can be used to visualize molecular dynamics systems. VMD has been - used to generate all the images of molecular systems here. - -.. container:: justify - - The goal of this extra tutorial is to provide some tips - to make good-looking pictures and videos of molecular systems. - -.. include:: ../../non-tutorials/needhelp.rst - -.. include:: ../../non-tutorials/1.9.2.rst - -Practical example -================= - -.. container:: justify - - To follow this tutorial, |dump_download| this LAMMPS trajectory file, which - corresponds to a mixture of water and toluene. - -.. |dump_download| raw:: html - - download - -.. container:: justify - - The water molecules use *types* 1 and 2, and the toluene molecules use - *types* 3, 4, and 5. - -.. container:: justify - - With Ubuntu/Linux, the *lammptrj* file can be opened with VMD by typing in a - terminal: - -.. code-block:: bash - - vmd dump.lammpstrj - -.. container:: justify - - Otherwise, simply open VMD and import the *dump.lammpstrj* file manually - using *File -> New molecule*. - -.. container:: justify - - Go to *Display*, change the view to *Orthographic*, and unselect - *Depth Cueing*. - -.. container:: justify - - Still in *Display*, select - *Axes -> Off*. - -.. figure:: ../figures/vmd/vmd-tutorial/step1-dark.png - :alt: VMD tutorial for LAMMPS - :class: only-dark - -.. figure:: ../figures/vmd/vmd-tutorial/step1-light.png - :alt: VMD tutorial for LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: Initial system in the absence of depth cueing and with orthographic - view. - -The representation ------------------- - -.. container:: justify - - In the main windows of VMD, go to *Graphics, Representations*. - Within the *Selected Atoms* windows, - replace *all* - by *type 1*. - Here, *type 1* corresponds to the oxygen of the water molecule. - Change the *Drawing Method* - from *Lines* - to *VDW*. - Tune the *Sphere Scale* - to 0.9, and increase the resolution to 52. - -.. container:: justify - - Click on *Create Rep* to create a second representation for the hydrogen - of water, select *type 2*, - and change the *Sphere Scale* to 0.5. - -.. container:: justify - - Create a third representation for *type 3 4 5*, - i.e. all 3 atom types of toluene, respectively - carbon, hydrogen, and another carbon. - -.. container:: justify - - Choose *DynamicBonds* - and increase the *bond resolution* to 52. - With *DynamicBonds*, the ends of the bonds are rough. - To smooth out the representation, create the - fourth and last representation (*VDW* with - *Sphere Scale* 0.2) - for *types* 3 4 5*. - -.. figure:: ../figures/vmd/vmd-tutorial/step2-dark.png - :alt: VMD tutorial for LAMMPS - :class: only-dark - -.. figure:: ../figures/vmd/vmd-tutorial/step2-light.png - :alt: VMD tutorial for LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: Orthographic view of the system with improved representation. - -The colors ----------- - -.. container:: justify - - To change the colors, go to *Graphics, Colors*, - click on *Display*, - then *Background*, and choose - the color you prefer (white is better for publication, black - can be good looking on presentation with a dark background). - -.. container:: justify - - Still in the *Color Controls* windows, - in *Categories*, - click *Name*. - In the *Names* sub windows - choose *3* (carbon) and select the color silver. Then, do - the same for 5 (also a carbon :math:`\to` silver), - 4 (hydrogen :math:`\to` white), - 2 (hydrogen :math:`\to` white), - 1 (oxygen :math:`\to` cyan). - -.. container:: justify - - Note that the cyan color is not standard for - oxygen. Feel free to change it based on your taste. - -.. container:: justify - - Let us slightly change the original *cyan* of VMD - by entering - manually the values 0.3, 1.0 and 1.0 in the RGB box. - -.. figure:: ../figures/vmd/vmd-tutorial/step3-dark.png - :alt: VMD tutorial for LAMMPS - :class: only-dark - -.. figure:: ../figures/vmd/vmd-tutorial/step3-light.png - :alt: VMD tutorial for LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: Orthographic view with improved representation and color. - -The materials -------------- - -.. container:: justify - - In the *Representations* windows, you can choose - among several materials that are more or less shiny - or opaque. - -.. container:: justify - - Let us select the default material named *Opaque*, - and change *Diffuse*, - *Specular*, and - *Shininess*, to 0.56, 0.12, and 0.29, respectively. - -.. figure:: ../figures/vmd/vmd-tutorial/step4-dark.png - :alt: VMD tutorial for LAMMPS - :class: only-dark - -.. figure:: ../figures/vmd/vmd-tutorial/step4-light.png - :alt: VMD tutorial for LAMMPS - :class: only-light - -.. container:: figurelegend - - Figure: Orthographic view with improved representation, color, and material. - See the corresponding |vmd_video|. - -.. |vmd_video| raw:: html - - video - -Additional options and rendering -================================ - -Transparent field ------------------ - -.. container:: justify - - A great representation offered by VMD is the *Quick surf*, - that can be combined with *transparent* material. - -.. container:: justify - - Here I turned off *Light 0*, - and turned on all three other default lights. - -.. figure:: ../figures/vmd/vmd-tutorial/transparent-dark.png - :alt: VMD tutorial for LAMMPS - transparent field - :class: only-dark - -.. figure:: ../figures/vmd/vmd-tutorial/transparent-light.png - :alt: VMD tutorial for LAMMPS - transparent field - :class: only-light - -.. container:: figurelegend - - Figure: System with water represented as a transparent field. - -Goodsell --------- - -.. container:: justify - - VMD also offers the Goodsell cartoon-like representation, - which can be an interesting alternative. - -.. figure:: ../figures/vmd/vmd-tutorial/goodsell-dark.png - :alt: VMD tutorial for LAMMPS - System in the style of David Goodsell - :class: only-dark - -.. figure:: ../figures/vmd/vmd-tutorial/goodsell-light.png - :alt: VMD tutorial for LAMMPS - System in the style of David Goodsell - :class: only-light - -.. container:: figurelegend - - Figure: System in the style of David Goodsell. - -Box border ----------- - -.. container:: justify - - Optionally, you can visualize the borders of the simulation - box by typing in the VMD terminal: - -.. code-block:: bash - - pbc box -center origin -color black -width 2 - -Saving a state --------------- - -.. container:: justify - - To avoid redoing all these steps every time - VMD is re-opened, one can save the VMD state by - clicking *File → Save vizualisation state*. - This state can then be used simply by clicking *File* - :math:`\to` - *Load vizualisation state*. - -Rendering image ---------------- - -.. container:: justify - - To generate high a resolution image, go in *File → Render*, - choose *Tachyon*, - hit *Start Rendering*. - -Rendering movie ---------------- - -.. container:: justify - - To generate a high-resolution movie, go into *Extension, Vizualisation*, - and *Movie Maker*. - -.. container:: justify - - If you hit *Make Movie* directly, the movie generated by VMD will be - of poor quality. - Instead, it is better to generate a sequence of high-resolution - images, and assemble these images. - -.. container:: justify - - Go in *Movie Settings*, hit *Trajectory* (so the movie will show - the system evolving in time, and not rotating on itself), - Uncheck *Delete image files*. - In *Rendered*, choose *Tachyon*, - then hit *Make Movie*. - -.. container:: justify - - From the Linux terminal (not the VMD terminal), assemble the images - (all starting with *untitled*) into a single movie by typing: - -.. code-block:: bash - - ffmpeg -r 60 -i untitled.%05d.ppm -vcodec libx264 \ - -crf 0 -pix_fmt yuv420p myvideo.mp4 - -.. container:: justify - - You may receive the following error: - -.. code-block:: bash - - width not divisible by 2 (1363x1134) - -.. container:: justify - - In that case, simply remove one line of pixel with the command: - -.. code-block:: bash - - for file in untitled.*.ppm; do convert $file -crop 1362x1134+0+0 $file; done - -.. container:: justify - - To convert the video in *webp*, for web integration, use: - -.. code-block:: bash - - ffmpeg -i myvideo.mp4 -vcodec libwebp -filter:v fps=fps=20 \ - -lossless 1 -loop 0 -preset default -an -vsync 0 myvideo.webp