diff --git a/.gitignore b/.gitignore
index 5bb7f95f..31ced741 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,6 +6,9 @@ dptb/tests/**/*.pth
 dptb/tests/**/*.npy
 dptb/tests/**/*.traj
 dptb/tests/**/out*/*
+dptb/tests/**/out*/*
+dptb/tests/**/*lmdb
+dptb/tests/**/*h5
 examples/_*
 *.dat
 *log*
diff --git a/dptb/__main__.py b/dptb/__main__.py
index c3b188d8..2cc41d23 100644
--- a/dptb/__main__.py
+++ b/dptb/__main__.py
@@ -1,5 +1,39 @@
-from dptb.entrypoints.main import main
+from dptb.entrypoints.main import main as entry_main
+import logging
+import pyfiglet
+from dptb import __version__
 
+logging.basicConfig(level=logging.INFO, format='%(message)s')
+log = logging.getLogger(__name__)
+
+def print_logo():
+    f = pyfiglet.Figlet(font='dos_rebel')  # 您可以选择您喜欢的字体
+    logo = f.renderText("DeePTB")
+    log.info(" ")
+    log.info(" ")
+    log.info("#"*81)
+    log.info("#" + " "*79 + "#")
+    log.info("#" + " "*79 + "#")
+    for line in logo.split('\n'):
+        if line.strip():  # 避免记录空行
+            log.info('#     '+line+ '     #')
+    log.info("#" + " "*79 + "#")
+    version_info = f"Version: {__version__}"
+    padding = (79 - len(version_info)) // 2
+    nspace = 79-padding
+    format_str = "#" + "{}"+"{:<"+f"{nspace}" + "}"+ "#"         
+    log.info(format_str.format(" "*padding, version_info))
+    log.info("#" + " "*79 + "#")
+    log.info("#"*81)
+    log.info(" ")
+    log.info(" ")
+def main() -> None:
+    """
+    The main entry point for the dptb package.
+    """
+    print_logo()
+    entry_main()
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    #print_logo()
+    main()
diff --git a/dptb/data/AtomicData.py b/dptb/data/AtomicData.py
index cf7447b1..0017070b 100644
--- a/dptb/data/AtomicData.py
+++ b/dptb/data/AtomicData.py
@@ -496,7 +496,7 @@ def from_points(
     def from_ase(
         cls,
         atoms,
-        r_max,
+        r_max: Union[float, int, dict],
         er_max: Optional[float] = None,
         oer_max: Optional[float] = None,
         key_mapping: Optional[Dict[str, str]] = {},
diff --git a/dptb/data/build.py b/dptb/data/build.py
index d2c6e56a..2f121e7a 100644
--- a/dptb/data/build.py
+++ b/dptb/data/build.py
@@ -3,7 +3,7 @@
 from copy import deepcopy
 import glob
 from importlib import import_module
-
+from typing import Union
 from dptb.data.dataset import DefaultDataset
 from dptb.data.dataset._deeph_dataset import DeePHE3Dataset
 from dptb.data.dataset._hdf5_dataset import HDF5Dataset
@@ -14,7 +14,13 @@
 from dptb.utils import instantiate, get_w_prefix
 from dptb.utils.tools import j_loader
 from dptb.utils.argcheck import normalize_setinfo, normalize_lmdbsetinfo
+from dptb.utils.argcheck import collect_cutoffs 
+import logging
+import torch
+import copy
+
 
+log = logging.getLogger(__name__)
 
 def dataset_from_config(config, prefix: str = "dataset") -> AtomicDataset:
     """initialize database based on a config instance
@@ -105,10 +111,17 @@ def dataset_from_config(config, prefix: str = "dataset") -> AtomicDataset:
 
     return instance
 
+class DatasetBuilder:
+    def __init__(self):
+        pass
 
-def build_dataset(
+    def __call__(self, 
         # set_options
         root: str,
+        # dataset_options
+        r_max: Union[float, int, dict],
+        er_max: Union[float, int, dict] = None,
+        oer_max: Union[float, int, dict] = None,
         type: str = "DefaultDataset",
         prefix: str = None,
         separator:str='.',
@@ -116,156 +129,251 @@ def build_dataset(
         get_overlap: bool = False,
         get_DM: bool = False,
         get_eigenvalues: bool = False,
-
         # common_options
         orthogonal: bool = False,
         basis: str = None, 
         **kwargs,
         ):
     
-    """
-    Build a dataset based on the provided set options and common options.
-
-    Args:
-        - type (str): The type of dataset to build. Default is "DefaultDataset".
-        - root (str): The main directory storing all trajectory folders.
-        - prefix (str, optional): Load selected trajectory folders with the specified prefix.
-        - get_Hamiltonian (bool, optional): Load the Hamiltonian file to edges of the graph or not.
-        - get_eigenvalues (bool, optional): Load the eigenvalues to the graph or not.
-        e.g.     
-        type = "DefaultDataset",
-        root = "foo/bar/data_files_here",
-        prefix = "set"
-
-        - basis (str, optional): The basis for the OrbitalMapper.
-
-    Returns:
-        dataset: The built dataset.
-
-    Raises:
-        ValueError: If the dataset type is not supported.
-        Exception: If the info.json file is not properly provided for a trajectory folder.
-    """
-    dataset_type = type
-    # See if we can get a OrbitalMapper.
-    if basis is not None:
-        idp = OrbitalMapper(basis=basis)
-    else:
-        idp = None
-
-    if dataset_type in ["DefaultDataset", "DeePHDataset", "HDF5Dataset", "LMDBDataset"]:
-
-        # Explore the dataset's folder structure.
-        #include_folders = []
-        #for dir_name in os.listdir(root):
-        #    dir_path = os.path.join(root, dir_name)
-        #    if os.path.isdir(dir_path):
-        #        # If the `processed_dataset` or other folder is here too, they do not have the proper traj data files.
-        #        # And we will have problem in generating TrajData! 
-        #        # So we test it here: the data folder must have `.dat` or `.traj` file.
-        #        # If not, we will skip thi
-        #        if glob.glob(os.path.join(dir_path, '*.dat')) or glob.glob(os.path.join(dir_path, '*.traj')):
-        #            if prefix is not None:
-        #                if dir_name[:len(prefix)] == prefix:
-        #                    include_folders.append(dir_name)
-        #            else:
-        #                include_folders.append(dir_name)
-
-        assert prefix is not None, "The prefix is not provided. Please provide the prefix to select the trajectory folders."
-        prefix_folders = glob.glob(f"{root}/{prefix}{separator}*")
-        include_folders=[]
-        for idir in prefix_folders:
-            if os.path.isdir(idir):
-                if not glob.glob(os.path.join(idir, '*.dat')) \
-                    and not glob.glob(os.path.join(idir, '*.traj')) \
-                        and not glob.glob(os.path.join(idir, '*.h5')) \
-                            and not glob.glob(os.path.join(idir, '*.mdb')):
-                    raise Exception(f"{idir} does not have the proper traj data files. Please check the data files.")
-                include_folders.append(idir.split('/')[-1])
-        
-        assert isinstance(include_folders, list) and len(include_folders) > 0, "No trajectory folders are found. Please check the prefix."                
-            
-        # We need to check the `info.json` very carefully here.
-        # Different `info` points to different dataset, 
-        # even if the data files in `root` are basically the same.
-        info_files = {}
-
-        # See if a public info is provided.
-        #if "info.json" in os.listdir(root):
-        if os.path.exists(f"{root}/info.json"):
-            public_info = j_loader(os.path.join(root, "info.json"))
-            if dataset_type == "LMDBDataset":
-                public_info = normalize_lmdbsetinfo(public_info)
-            else:
-                public_info = normalize_setinfo(public_info)
-            print("A public `info.json` file is provided, and will be used by the subfolders who do not have their own `info.json` file.")
+        """
+        Build a dataset based on the provided set options and common options.
+
+        Args:
+            - type (str): The type of dataset to build. Default is "DefaultDataset".
+            - root (str): The main directory storing all trajectory folders.
+            - prefix (str, optional): Load selected trajectory folders with the specified prefix.
+            - get_Hamiltonian (bool, optional): Load the Hamiltonian file to edges of the graph or not.
+            - get_eigenvalues (bool, optional): Load the eigenvalues to the graph or not.
+            e.g.     
+            type = "DefaultDataset",
+            root = "foo/bar/data_files_here",
+            prefix = "set"
+
+            - basis (str, optional): The basis for the OrbitalMapper.
+
+        Returns:
+            dataset: The built dataset.
+
+        Raises:
+            ValueError: If the dataset type is not supported.
+            Exception: If the info.json file is not properly provided for a trajectory folder.
+        """
+        self.r_max = r_max
+        self.er_max = er_max
+        self.oer_max = oer_max
+
+        self.if_check_cutoffs = False
+
+        dataset_type = type
+        # See if we can get a OrbitalMapper.
+        if basis is not None:
+            idp = OrbitalMapper(basis=basis)
         else:
-            public_info = None
-
-        # Load info in each trajectory folders seperately.
-        for file in include_folders:
-            #if "info.json" in os.listdir(os.path.join(root, file)):
-            if os.path.exists(f"{root}/{file}/info.json"):
-                # use info provided in this trajectory.
-                info = j_loader(f"{root}/{file}/info.json")
+            idp = None
+
+        if dataset_type in ["DefaultDataset", "DeePHDataset", "HDF5Dataset", "LMDBDataset"]:
+            assert prefix is not None, "The prefix is not provided. Please provide the prefix to select the trajectory folders."
+            prefix_folders = glob.glob(f"{root}/{prefix}{separator}*")
+            include_folders=[]
+            for idir in prefix_folders:
+                if os.path.isdir(idir):
+                    if not glob.glob(os.path.join(idir, '*.dat')) \
+                        and not glob.glob(os.path.join(idir, '*.traj')) \
+                            and not glob.glob(os.path.join(idir, '*.h5')) \
+                                and not glob.glob(os.path.join(idir, '*.mdb')):
+                        raise Exception(f"{idir} does not have the proper traj data files. Please check the data files.")
+                    include_folders.append(idir.split('/')[-1])
+
+            assert isinstance(include_folders, list) and len(include_folders) > 0, "No trajectory folders are found. Please check the prefix."                
+
+            # We need to check the `info.json` very carefully here.
+            # Different `info` points to different dataset, 
+            # even if the data files in `root` are basically the same.
+            info_files = {}
+
+            # See if a public info is provided.
+            #if "info.json" in os.listdir(root):
+            if os.path.exists(f"{root}/info.json"):
+                public_info = j_loader(os.path.join(root, "info.json"))
                 if dataset_type == "LMDBDataset":
-                    info = normalize_lmdbsetinfo(info)
+                    public_info = {}
+                    log.info("A public `info.json` file is provided, but will not be used  anymore for LMDBDataset.")
                 else:
-                    info = normalize_setinfo(info)
-                info_files[file] = info
-            elif public_info is not None:
-                # use public info instead
-                # yaml will not dump correctly if this is not a deepcopy.
-                info_files[file] = deepcopy(public_info)
+                    public_info = normalize_setinfo(public_info)
+                    log.info("A public `info.json` file is provided, and will be used by the subfolders who do not have their own `info.json` file.")
             else:
-                # no info for this file
-                raise Exception(f"info.json is not properly provided for `{file}`.")
-            
-        # We will sort the info_files here.
-        # The order itself is not important, but must be consistant for the same list.
-        info_files = {key: info_files[key] for key in sorted(info_files)}
+                public_info = None
+
+            # Load info in each trajectory folders seperately.
+            for file in include_folders:
+                #if "info.json" in os.listdir(os.path.join(root, file)):
+
+                if dataset_type == "LMDBDataset":
+                    info_files[file] = {}
+                elif os.path.exists(f"{root}/{file}/info.json"):
+                    # use info provided in this trajectory.
+                    info = j_loader(f"{root}/{file}/info.json")
+                    info = normalize_setinfo(info)
+                    info_files[file] = info
+                elif public_info is not None:  # not lmbd and no info in subfolder, then must use public info.
+                    # use public info instead
+                    # yaml will not dump correctly if this is not a deepcopy.
+                    info_files[file] = deepcopy(public_info)
+                else:  # not lmdb no info in subfolder and no public info. then raise error.
+                    log.error(f"for {dataset_type} type, the info.json is not properly provided for `{file}`")
+                    raise ValueError(f"for {dataset_type} type, the info.json is not properly provided for `{file}`")
+
+            # We will sort the info_files here.
+            # The order itself is not important, but must be consistant for the same list.
+            info_files = {key: info_files[key] for key in sorted(info_files)}
         
-        if dataset_type == "DeePHDataset":
-            dataset = DeePHE3Dataset(
-                root=root,
-                type_mapper=idp,
-                get_Hamiltonian=get_Hamiltonian,
-                get_eigenvalues=get_eigenvalues,
-                info_files = info_files
-            )
-        elif dataset_type == "DefaultDataset":
-            dataset = DefaultDataset(
+            for ikey in info_files:
+                info_files[ikey].update({'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max})
+
+            if dataset_type == "DeePHDataset":
+                dataset = DeePHE3Dataset(
+                    root=root,
+                    type_mapper=idp,
+                    get_Hamiltonian=get_Hamiltonian,
+                    get_eigenvalues=get_eigenvalues,
+                    info_files = info_files
+                )
+            elif dataset_type == "DefaultDataset":
+                dataset = DefaultDataset(
+                    root=root,
+                    type_mapper=idp,
+                    get_Hamiltonian=get_Hamiltonian,
+                    get_overlap=get_overlap,
+                    get_DM=get_DM,
+                    get_eigenvalues=get_eigenvalues,
+                    info_files = info_files
+                )
+            elif dataset_type == "HDF5Dataset":
+                dataset = HDF5Dataset(
+                    root=root,
+                    type_mapper=idp,
+                    get_Hamiltonian=get_Hamiltonian,
+                    get_overlap=get_overlap,
+                    get_DM=get_DM,
+                    get_eigenvalues=get_eigenvalues,
+                    info_files = info_files
+                )
+            elif dataset_type == "LMDBDataset":
+                dataset = LMDBDataset(
                 root=root,
                 type_mapper=idp,
+                orthogonal=orthogonal,
                 get_Hamiltonian=get_Hamiltonian,
                 get_overlap=get_overlap,
                 get_DM=get_DM,
                 get_eigenvalues=get_eigenvalues,
                 info_files = info_files
             )
-        elif dataset_type == "HDF5Dataset":
-            dataset = HDF5Dataset(
-                root=root,
-                type_mapper=idp,
-                get_Hamiltonian=get_Hamiltonian,
-                get_overlap=get_overlap,
-                get_DM=get_DM,
-                get_eigenvalues=get_eigenvalues,
-                info_files = info_files
-            )
-        elif dataset_type == "LMDBDataset":
-            dataset = LMDBDataset(
-            root=root,
-            type_mapper=idp,
-            orthogonal=orthogonal,
-            get_Hamiltonian=get_Hamiltonian,
-            get_overlap=get_overlap,
-            get_DM=get_DM,
-            get_eigenvalues=get_eigenvalues,
-            info_files = info_files
+
+        else:
+            raise ValueError(f"Not support dataset type: {type}.")
+        
+        if not self.if_check_cutoffs:
+            log.warning("The cutoffs in data and model are not checked. be careful!")
+
+        return dataset
+    
+    def from_model(self, 
+               model, 
+               root: str,
+               type: str = "DefaultDataset",
+               prefix: str = None,
+               separator:str='.',
+               get_Hamiltonian: bool = False,
+               get_overlap: bool = False,
+               get_DM: bool = False,
+               get_eigenvalues: bool = False,
+               # common_options
+               orthogonal: bool = False,
+               basis: str = None, 
+               **kwargs):
+        """
+        Build a dataset from a model.
+
+        Args:
+            - model (torch.nn.Module): The model to build the dataset from.
+            - dataset_type (str, optional): The type of dataset to build. Default is "DefaultDataset".
+
+        Returns:
+            dataset: The built dataset.
+        """
+        cutoff_options = collect_cutoffs(model.model_options)
+    
+        dataset = self(
+            root = root,
+            **cutoff_options,
+            type = type,
+            prefix = prefix,
+            separator = separator,
+            get_Hamiltonian = get_Hamiltonian,
+            get_overlap = get_overlap,
+            get_DM = get_DM,
+            get_eigenvalues = get_eigenvalues,
+            orthogonal = orthogonal,
+            basis = basis, 
+            **kwargs,
         )
 
-    else:
-        raise ValueError(f"Not support dataset type: {type}.")
+        return dataset
+
+    def check_cutoffs(self,model:torch.nn.Module=None, **kwargs):
+        if model is None:
+            self.if_check_cutoffs = False
+            log.warning("No model is provided. We can not check the cutoffs used in data and model are consistent.")
+        else:
+            self.if_check_cutoffs = True
+            cutoff_options = collect_cutoffs(model.model_options)
+            if isinstance(cutoff_options['r_max'],dict):
+                assert isinstance(self.r_max,dict), "The r_max in model is a dict, but in dataset it is not."
+                for key in cutoff_options['r_max']:
+                    if key not in self.r_max:
+                        log.error(f"The key {key} in r_max is not defined in dataset")
+                        raise ValueError(f"The key {key} in r_max is not defined in dataset")
+                    assert self.r_max >=  cutoff_options['r_max'][key], f"The r_max in model shoule be  smaller than in dataset for {key}."
+
+            elif isinstance(cutoff_options['r_max'],float):
+                assert isinstance(self.r_max,float), "The r_max in model is a float, but in dataset it is not."
+                assert self.r_max >=  cutoff_options['r_max'], "The r_max in model shoule be  smaller than in dataset."        
+
+            if isinstance(cutoff_options['er_max'],dict):
+                assert isinstance(self.er_max,dict), "The er_max in model is a dict, but in dataset it is not."
+                for key in cutoff_options['er_max']:
+                    if key not in self.er_max:
+                        log.error(f"The key {key} in er_max is not defined in dataset")
+                        raise ValueError(f"The key {key} in er_max is not defined in dataset")
+                    
+                    assert self.er_max >=  cutoff_options['er_max'][key], f"The er_max in model shoule be  smaller than in dataset for {key}."
+
+            elif isinstance(cutoff_options['er_max'],float):
+                assert isinstance(self.er_max,float), "The er_max in model is a float, but in dataset it is not."
+                assert self.er_max >=  cutoff_options['er_max'], "The er_max in model shoule be  smaller than in dataset."
+            elif cutoff_options['er_max'] is None:
+                assert self.er_max is None, "The er_max in model is None, but in dataset it is not."
+
+            
+            if isinstance(cutoff_options['oer_max'],dict):
+                assert isinstance(self.oer_max,dict), "The oer_max in model is a dict, but in dataset it is not."
+                for key in cutoff_options['oer_max']:
+                    if key not in self.oer_max:
+                        log.error(f"The key {key} in oer_max is not defined in dataset")
+                        raise ValueError(f"The key {key} in oer_max is not defined in dataset")
+                    
+                    assert self.oer_max >=  cutoff_options['oer_max'][key], f"The oer_max in model shoule be  smaller than in dataset for {key}."
+            elif isinstance(cutoff_options['oer_max'],float):
+                assert isinstance(self.oer_max,float), "The oer_max in model is a float, but in dataset it is not."
+                assert self.oer_max >=  cutoff_options['oer_max'], "The oer_max in model shoule be  smaller than in dataset."
+            elif cutoff_options['oer_max'] is None:
+                assert self.oer_max is None, "The oer_max in model is None, but in dataset it is not."
+                
+# note, compared to the previous build_dataset, this one is more flexible.    
+# previous build_dataset is a function. now i define a class DataBuilder and re-defined __call__ function.
+# then build_dataset is an instance of DataBuilder class. so i can use build_dataset.from_model() to build dataset from model.
+# at the same time the previous way to use  build_dataset is still available. like build_dataset(...).
+
+build_dataset = DatasetBuilder()
 
-    return dataset
\ No newline at end of file
diff --git a/dptb/data/dataset/_deeph_dataset.py b/dptb/data/dataset/_deeph_dataset.py
index 0aee98e5..9e540e32 100644
--- a/dptb/data/dataset/_deeph_dataset.py
+++ b/dptb/data/dataset/_deeph_dataset.py
@@ -43,13 +43,11 @@ def __init__(
         for file in self.info_files.keys():
             # get the info here
             info = info_files[file]
-            assert "AtomicData_options" in info
-            AtomicData_options = info["AtomicData_options"]
-            assert "r_max" in AtomicData_options
-            assert "pbc" in AtomicData_options
+            assert "r_max" in info
+            assert "pbc" in info
             subdata = os.path.join(self.root, file)
             self.raw_data.append(subdata)
-            self.data_options[subdata] = AtomicData_options
+            self.data_options[subdata] = info
 
         # The AtomicData_options is never used here.
         # Because we always return a list of AtomicData object in `get_data()`.
@@ -68,12 +66,15 @@ def get_data(self):
         for subpath in tqdm(self.raw_data, desc="Loading data"):
             # the type_mapper here is loaded in PyG `dataset` type as `transform` attritube
             # so the OrbitalMapper can be accessed by self.transform here
-            AtomicData_options = self.data_options[subpath]
+            info = self.data_options[subpath]
             atomic_data = AtomicData.from_points(
                 pos = np.loadtxt(os.path.join(subpath, "site_positions.dat")).T,
                 cell = np.loadtxt(os.path.join(subpath, "lat.dat")).T,
                 atomic_numbers = np.loadtxt(os.path.join(subpath, "element.dat")),
-                **AtomicData_options,
+                pbc = info["pbc"],
+                r_max=info["r_max"],
+                er_max=info.get("er_max", None),
+                oer_max=info.get("oer_max", None)
             )
             idp = self.type_mapper
             openmx_to_deeptb(atomic_data, idp, os.path.join(subpath, "./hamiltonians.h5"))
diff --git a/dptb/data/dataset/_default_dataset.py b/dptb/data/dataset/_default_dataset.py
index 1a18480d..94d34b43 100644
--- a/dptb/data/dataset/_default_dataset.py
+++ b/dptb/data/dataset/_default_dataset.py
@@ -21,6 +21,9 @@
 from dptb.data.AtomicDataDict import with_edge_vectors
 from dptb.nn.hamiltonian import E3Hamiltonian
 from tqdm import tqdm
+import logging
+
+log = logging.getLogger(__name__)
 
 class _TrajData(object):
     '''
@@ -40,71 +43,18 @@ class _TrajData(object):
     
     def __init__(self, 
                  root: str, 
-                 AtomicData_options: Dict[str, Any] = {},
+                 data ={},
                  get_Hamiltonian = False,
                  get_overlap = False,
                  get_DM = False,
                  get_eigenvalues = False,
-                 info = None,
-                 _clear = False):
+                 info = None):
         
         assert not get_Hamiltonian * get_DM, "Hamiltonian and Density Matrix can only loaded one at a time, for which will occupy the same attribute in the AtomicData."
         self.root = root
-        self.AtomicData_options = AtomicData_options
         self.info = info
+        self.data = data
 
-        self.data = {}
-        # load cell
-        
-        pbc = AtomicData_options["pbc"]
-        if isinstance(pbc, bool):
-            has_cell = pbc
-        elif isinstance(pbc, list):
-            has_cell = any(pbc)
-        else:
-            raise ValueError("pbc must be bool or list.")
-        
-        if has_cell:
-            cell = np.loadtxt(os.path.join(root, "cell.dat"))
-            if cell.shape[0] == 3:
-                # same cell size, then copy it to all frames.
-                cell = np.expand_dims(cell, axis=0)
-                self.data["cell"] = np.broadcast_to(cell, (self.info["nframes"], 3, 3))
-            elif cell.shape[0] == self.info["nframes"] * 3:
-                self.data["cell"] = cell.reshape(self.info["nframes"], 3, 3)
-            else:
-                raise ValueError("Wrong cell dimensions.")
-        
-        # load positions, stored as cartesion no matter what provided.
-        pos = np.loadtxt(os.path.join(root, "positions.dat"))
-        if len(pos.shape) == 1:
-            pos = pos.reshape(1,3)
-        natoms = self.info["natoms"]
-        if natoms < 0:
-            natoms = int(pos.shape[0] / self.info["nframes"])
-        assert pos.shape[0] == self.info["nframes"] * natoms
-        pos = pos.reshape(self.info["nframes"], natoms, 3)
-        # ase use cartesian by default.
-        if self.info["pos_type"] == "cart" or self.info["pos_type"] == "ase":
-            self.data["pos"] = pos
-        elif self.info["pos_type"] == "frac":
-            self.data["pos"] = pos @ self.data["cell"]
-        else:
-            raise NameError("Position type must be cart / frac.")
-        
-        # load atomic numbers
-        atomic_numbers = np.loadtxt(os.path.join(root, "atomic_numbers.dat"))
-        if atomic_numbers.shape == ():
-            atomic_numbers = atomic_numbers.reshape(1)
-        if atomic_numbers.shape[0] == natoms:
-            # same atomic_numbers, copy it to all frames.
-            atomic_numbers = np.expand_dims(atomic_numbers, axis=0)
-            self.data["atomic_numbers"] = np.broadcast_to(atomic_numbers, (self.info["nframes"], natoms))
-        elif atomic_numbers.shape[0] == natoms * self.info["nframes"]:
-            self.data["atomic_numbers"] = atomic_numbers.reshape(self.info["nframes"],natoms)
-        else:
-            raise ValueError("Wrong atomic_number dimensions.")
-        
         # load optional data files
         if get_eigenvalues == True:
             if os.path.exists(os.path.join(self.root, "eigenvalues.npy")):
@@ -146,16 +96,77 @@ def __init__(self,
             else:
                 self.data["DM_blocks"] = h5py.File(os.path.join(self.root, "DM.h5"), "r")
         
-        # this is used to clear the tmp files to load ase trajectory only.
-        if _clear:
-            os.remove(os.path.join(root, "positions.dat"))
-            os.remove(os.path.join(root, "cell.dat"))
-            os.remove(os.path.join(root, "atomic_numbers.dat"))
-
+    @classmethod
+    def from_text_data(cls,
+                       root: str, 
+                       get_Hamiltonian = False,
+                       get_overlap = False,
+                       get_DM = False,
+                       get_eigenvalues = False,
+                       info = None):
+
+        data = {}
+        pbc = info["pbc"]
+        # load cell        
+        if isinstance(pbc, bool):
+            has_cell = pbc
+        elif isinstance(pbc, list):
+            has_cell = any(pbc)
+        else:
+            raise ValueError("pbc must be bool or list.")
+        
+        if has_cell:
+            cell = np.loadtxt(os.path.join(root, "cell.dat"))
+            if cell.shape[0] == 3:
+                # same cell size, then copy it to all frames.
+                cell = np.expand_dims(cell, axis=0)
+                data["cell"] = np.broadcast_to(cell, (info["nframes"], 3, 3))
+            elif cell.shape[0] == info["nframes"] * 3:
+                data["cell"] = cell.reshape(info["nframes"], 3, 3)
+            else:
+                raise ValueError("Wrong cell dimensions.")
+        
+        # load positions, stored as cartesion no matter what provided.
+        pos = np.loadtxt(os.path.join(root, "positions.dat"))
+        if len(pos.shape) == 1:
+            pos = pos.reshape(1,3)
+        natoms = info["natoms"]
+        if natoms < 0:
+            natoms = int(pos.shape[0] / info["nframes"])
+        assert pos.shape[0] == info["nframes"] * natoms
+        pos = pos.reshape(info["nframes"], natoms, 3)
+        # ase use cartesian by default.
+        if info["pos_type"] == "cart" or info["pos_type"] == "ase":
+            data["pos"] = pos
+        elif info["pos_type"] == "frac":
+            data["pos"] = pos @ data["cell"]
+        else:
+            raise NameError("Position type must be cart / frac.")
+        
+        # load atomic numbers
+        atomic_numbers = np.loadtxt(os.path.join(root, "atomic_numbers.dat"))
+        if atomic_numbers.shape == ():
+            atomic_numbers = atomic_numbers.reshape(1)
+        if atomic_numbers.shape[0] == natoms:
+            # same atomic_numbers, copy it to all frames.
+            atomic_numbers = np.expand_dims(atomic_numbers, axis=0)
+            data["atomic_numbers"] = np.broadcast_to(atomic_numbers, (info["nframes"], natoms))
+        elif atomic_numbers.shape[0] == natoms * info["nframes"]:
+            data["atomic_numbers"] = atomic_numbers.reshape(info["nframes"],natoms)
+        else:
+            raise ValueError("Wrong atomic_number dimensions.")
+        
+        return cls(root=root,
+                   data=data,
+                   get_Hamiltonian=get_Hamiltonian,
+                   get_overlap=get_overlap,
+                   get_DM=get_DM,
+                   get_eigenvalues=get_eigenvalues,
+                   info=info)
+    
     @classmethod
     def from_ase_traj(cls,
                       root: str, 
-                      AtomicData_options: Dict[str, Any] = {},
                       get_Hamiltonian = False,
                       get_overlap = False,
                       get_DM = False,
@@ -167,31 +178,63 @@ def from_ase_traj(cls,
         traj_file = glob.glob(f"{root}/*.traj")
         assert len(traj_file) == 1, print("only one ase trajectory file can be provided.")
         traj = Trajectory(traj_file[0], 'r')
+        nframes = len(traj)
+        assert nframes > 0, print("trajectory file is empty.")
+        if nframes != info.get("nframes", None):
+            info['nframes'] = nframes   
+            log.info(f"Number of frames ({nframes}) in trajectory file does not match the number of frames in info file.")
+        
+        natoms = traj[0].positions.shape[0]
+        if natoms != info["natoms"]:
+            info["natoms"] = natoms
+
+        pbc = info.get("pbc",None)
+        if pbc is None:
+            pbc = traj[0].pbc.tolist()
+            info["pbc"] = pbc
+        
+        if isinstance(pbc, bool):
+            pbc = [pbc] * 3
+
+        if pbc != traj[0].pbc.tolist():
+            log.warning("!! PBC setting in info file does not match the PBC setting in trajectory file, we use the one in info json. BE CAREFUL!")
+        
         positions = []
         cell = []
         atomic_numbers = []
+
         for atoms in traj:
             positions.append(atoms.get_positions())
-            cell.append(atoms.get_cell())
+            
             atomic_numbers.append(atoms.get_atomic_numbers())
+            if (np.abs(atoms.get_cell()-np.zeros([3,3]))< 1e-6).all():
+                cell = None
+            else:
+                cell.append(atoms.get_cell())
+
         positions = np.array(positions)
-        positions = positions.reshape(-1, 3)
-        cell = np.array(cell)
-        cell = cell.reshape(-1, 3)
+        positions = positions.reshape(nframes,natoms, 3)
+        
+        if cell is not None:
+            cell = np.array(cell)
+            cell = cell.reshape(nframes,3, 3)
+        
         atomic_numbers = np.array(atomic_numbers)
-        atomic_numbers = atomic_numbers.reshape(-1, 1)
-        np.savetxt(os.path.join(root, "positions.dat"), positions)
-        np.savetxt(os.path.join(root, "cell.dat"), cell)
-        np.savetxt(os.path.join(root, "atomic_numbers.dat"), atomic_numbers, fmt='%d')
+        atomic_numbers = atomic_numbers.reshape(nframes, natoms)
+
+        data = {}
+        if cell is not None:
+            data["cell"] = cell
+        data["pos"] = positions 
+        data["atomic_numbers"] = atomic_numbers
 
         return cls(root=root,
-                   AtomicData_options=AtomicData_options,
+                   data=data,
                    get_Hamiltonian=get_Hamiltonian,
                    get_overlap=get_overlap,
                    get_DM=get_DM,
                    get_eigenvalues=get_eigenvalues,
-                   info=info,
-                   _clear=True)
+                   info=info)
         
     def toAtomicDataList(self, idp: TypeMapper = None):
         data_list = []
@@ -218,10 +261,11 @@ def toAtomicDataList(self, idp: TypeMapper = None):
                                                                                   dtype=torch.long)
 
             atomic_data = AtomicData.from_points(
+                  r_max = self.info["r_max"],
+                  pbc = self.info["pbc"],
+                  er_max = self.info.get("er_max", None),
+                  oer_max= self.info.get("oer_max", None),
                   **kwargs,
-                  # pbc is stored in AtomicData_options now.
-                  #pbc = self.info["pbc"], 
-                  **self.AtomicData_options
             )
             if "hamiltonian_blocks" in self.data:
                 assert idp is not None, "LCAO Basis must be provided  in `common_option` for loading Hamiltonian."
@@ -300,21 +344,19 @@ def __init__(
         for file in self.info_files.keys():
             # get the info here
             info = info_files[file]
-            assert "AtomicData_options" in info
-            AtomicData_options = info["AtomicData_options"]
-            assert "r_max" in AtomicData_options
-            assert "pbc" in AtomicData_options
+            # assert "AtomicData_options" in info
+            assert "r_max" in info
+            assert "pbc" in info
+            pbc = info["pbc"]
             if info["pos_type"] == "ase":
                 subdata = _TrajData.from_ase_traj(os.path.join(self.root, file), 
-                                AtomicData_options,
                                 get_Hamiltonian, 
                                 get_overlap,
                                 get_DM,
                                 get_eigenvalues,
                                 info=info)
             else:
-                subdata = _TrajData(os.path.join(self.root, file), 
-                                AtomicData_options,
+                subdata = _TrajData.from_text_data(os.path.join(self.root, file), 
                                 get_Hamiltonian,
                                 get_overlap,
                                 get_DM,
diff --git a/dptb/data/dataset/_hdf5_dataset.py b/dptb/data/dataset/_hdf5_dataset.py
index c6b853e8..ef726589 100644
--- a/dptb/data/dataset/_hdf5_dataset.py
+++ b/dptb/data/dataset/_hdf5_dataset.py
@@ -38,7 +38,6 @@ class _HDF5_TrajData(object):
 
     def __init__(self, 
                  root: str, 
-                 AtomicData_options: Dict[str, Any] = {},
                  get_Hamiltonian = False,
                  get_overlap = False,
                  get_DM = False,
@@ -46,9 +45,7 @@ def __init__(self,
                  info = None):
         assert not get_Hamiltonian * get_DM, "Cannot get both Hamiltonian and DM"
         self.root = root
-        self.AtomicData_options = AtomicData_options
         self.info = info
-
         self.data = {}
 
         assert os.path.exists(os.path.join(root, "structure.pkl")), "structure file not found."
@@ -87,9 +84,11 @@ def toAtomicDataList(self, idp: TypeMapper = None):
                 pos = self.data['structure'][frame]["positions"][:],
                 cell = frame_cell,
                 atomic_numbers = self.data['structure'][frame]["atomic_numbers"][:],
-                # pbc is stored in AtomicData_options now.
-                #pbc = self.info["pbc"], 
-                **self.AtomicData_options)
+                r_max = self.info["r_max"], 
+                er_max = self.info.get("er_max", None),
+                oer_max = self.info.get("oer_max", None),
+                pbc = self.info["pbc"], 
+            )
             
             if "hamiltonian_blocks" in self.data:
                 assert idp is not None, "LCAO Basis must be provided  in `common_option` for loading Hamiltonian."
@@ -171,13 +170,10 @@ def __init__(
         for file in self.info_files.keys():
             # get the info here
             info = info_files[file]
-            assert "AtomicData_options" in info
-            AtomicData_options = info["AtomicData_options"]
-            assert "r_max" in AtomicData_options
-            assert "pbc" in AtomicData_options
+            assert "r_max" in info
+            assert "pbc" in info
             if info["pos_type"] in ["hdf5", 'pickle']:
                 subdata = _HDF5_TrajData(os.path.join(self.root, file), 
-                                AtomicData_options,
                                 get_Hamiltonian, 
                                 get_overlap,
                                 get_DM,
diff --git a/dptb/entrypoints/train.py b/dptb/entrypoints/train.py
index 958dec4b..a9e5c7d7 100644
--- a/dptb/entrypoints/train.py
+++ b/dptb/entrypoints/train.py
@@ -3,7 +3,7 @@
 from dptb.data.build import build_dataset
 from dptb.plugins.monitor import TrainLossMonitor, LearningRateMonitor, Validationer
 from dptb.plugins.train_logger import Logger
-from dptb.utils.argcheck import normalize
+from dptb.utils.argcheck import normalize, collect_cutoffs
 from dptb.plugins.saver import Saver
 from typing import Dict, List, Optional, Any
 from dptb.utils.tools import j_loader, setup_seed, j_must_have
@@ -18,6 +18,7 @@
 import json
 import os
 import time
+import copy
 
 __all__ = ["train"]
 
@@ -147,12 +148,19 @@ def train(
                     jdata["train_options"] = f["config"]["train_options"]
                 if jdata.get("model_options") is None:
                     jdata["model_options"] = f["config"]["model_options"]
+                
+                ## add some warning !
+                for k, v in jdata["model_options"].items():
+                    if k not in f["config"]["model_options"]:
+                        log.warning(f"The model options {k} is not defined in checkpoint, set to {v}.")
+                    else:
+                        deep_dict_difference(k, v, f["config"]["model_options"])
             del f
     else:
         j_must_have(jdata, "model_options")
         j_must_have(jdata, "train_options")
 
-
+    cutoff_options =collect_cutoffs(jdata)
     # setup seed
     setup_seed(seed=jdata["common_options"]["seed"])
 
@@ -160,13 +168,13 @@ def train(
     #     json.dump(jdata, fp, indent=4)
 
     # build dataset
-    train_datasets = build_dataset(**jdata["data_options"]["train"], **jdata["common_options"])
+    train_datasets = build_dataset(**cutoff_options,**jdata["data_options"]["train"], **jdata["common_options"])
     if jdata["data_options"].get("validation"):
-        validation_datasets = build_dataset(**jdata["data_options"]["validation"], **jdata["common_options"])
+        validation_datasets = build_dataset(**cutoff_options, **jdata["data_options"]["validation"], **jdata["common_options"])
     else:
         validation_datasets = None
     if jdata["data_options"].get("reference"):
-        reference_datasets = build_dataset(**jdata["data_options"]["reference"], **jdata["common_options"])
+        reference_datasets = build_dataset(**cutoff_options, **jdata["data_options"]["reference"], **jdata["common_options"])
     else:
         reference_datasets = None
 
@@ -228,3 +236,26 @@ def train(
     log.info(f"wall time: {(end_time - start_time):.3f} s")
 
 
+def deep_dict_difference(base_key, expected_value, model_options):
+    """
+    递归地记录嵌套字典中的选项差异。
+    
+    :param base_key: 基础键名，用于构建警告消息的前缀。
+    :param expected_value: 期望的值，可能是字典或非字典类型。
+    :param model_options: 用于比较的模型选项字典。
+    """
+    target_dict= copy.deepcopy(model_options) # 防止修改原始字典
+    if isinstance(expected_value, dict):
+        for subk, subv in expected_value.items():
+            
+            if  not isinstance(target_dict.get(base_key, {}),dict):
+                log.warning(f"The model option {subk} in {base_key} is not defined in  checkpoint, set to {subv}.")
+            
+            elif subk not in target_dict.get(base_key, {}):
+                log.warning(f"The model option {subk} in {base_key} is not defined in  checkpoint, set to {subv}.")
+            else:
+                target2 = copy.deepcopy(target_dict[base_key])
+                deep_dict_difference(f"{subk}", subv, target2)
+    else:
+        if expected_value != target_dict[base_key]:
+            log.warning(f"The model option {base_key} is set to {expected_value}, but in checkpoint it is {target_dict[base_key]}, make sure it it correct!")
\ No newline at end of file
diff --git a/dptb/plugins/train_logger.py b/dptb/plugins/train_logger.py
index 7cc3684b..8a9a6f40 100644
--- a/dptb/plugins/train_logger.py
+++ b/dptb/plugins/train_logger.py
@@ -7,7 +7,7 @@
 class Logger(Plugin):
     alignment = 4
     # 不同字段之间的分隔符
-    separator = '#' * 160 
+    separator = '-' * 81 
     
     def __init__(self, fields, interval=None):
         if interval is None:
diff --git a/dptb/postprocess/bandstructure/band.py b/dptb/postprocess/bandstructure/band.py
index b8758dc6..88565166 100644
--- a/dptb/postprocess/bandstructure/band.py
+++ b/dptb/postprocess/bandstructure/band.py
@@ -170,7 +170,7 @@ def __init__(self, model:torch.nn.Module, results_path: str=None, use_gui: bool=
         self.results_path = results_path
         self.use_gui = use_gui
             
-    def get_bands(self, data: Union[AtomicData, ase.Atoms, str], kpath_kwargs: dict, AtomicData_options: dict={}):
+    def get_bands(self, data: Union[AtomicData, ase.Atoms, str], kpath_kwargs: dict, pbc:Union[bool,list]=None, AtomicData_options:dict=None):
         kline_type = kpath_kwargs['kline_type']
 
         # get  the ase structure
@@ -208,7 +208,7 @@ def get_bands(self, data: Union[AtomicData, ase.Atoms, str], kpath_kwargs: dict,
             log.error('Error, now, kline_type only support ase_kpath, abacus, or vasp.')
             raise ValueError
         
-        data, eigenvalues = self.get_eigs(data, klist, AtomicData_options)
+        data, eigenvalues = self.get_eigs(data=data, klist=klist, pbc=pbc, AtomicData_options=AtomicData_options)
         
 
         # get the E_fermi from data
@@ -229,7 +229,7 @@ def get_bands(self, data: Union[AtomicData, ase.Atoms, str], kpath_kwargs: dict,
         #     estimated_E_fermi = None
         if nel_atom is not None:
             data,estimated_E_fermi = self.get_fermi_level(data=data, nel_atom=nel_atom, \
-                        klist = klist, AtomicData_options=AtomicData_options)
+                        klist = klist, pbc=pbc, AtomicData_options=AtomicData_options)
         else:
             estimated_E_fermi = None
 
diff --git a/dptb/postprocess/elec_struc_cal.py b/dptb/postprocess/elec_struc_cal.py
index 96df0f72..d2b47ab7 100644
--- a/dptb/postprocess/elec_struc_cal.py
+++ b/dptb/postprocess/elec_struc_cal.py
@@ -9,6 +9,8 @@
 log = logging.getLogger(__name__)
 from dptb.data import AtomicData, AtomicDataDict
 from dptb.nn.energy import Eigenvalues
+from dptb.utils.argcheck import get_cutoffs_from_model_options
+from copy import deepcopy
 
 # This class `ElecStruCal`  is designed to calculate electronic structure properties such as
 # eigenvalues and Fermi energy based on provided input data and model. 
@@ -42,6 +44,10 @@ def __init__ (
         self.model.eval()
         self.overlap = hasattr(model, 'overlap')
 
+        if not self.model.transform:
+            log.error('The model.transform is not True, please check the model.')
+            raise RuntimeError('The model.transform is not True, please check the model.')
+        
         if self.overlap:
             self.eigv = Eigenvalues(
                 idp=model.idp,
@@ -57,8 +63,9 @@ def __init__ (
                 device=self.device,
                 dtype=model.dtype,
             )
-
-    def get_data(self,data: Union[AtomicData, ase.Atoms, str],AtomicData_options: dict={},device: Union[str, torch.device]=None):
+        r_max, er_max, oer_max  = get_cutoffs_from_model_options(model.model_options)
+        self.cutoffs = {'r_max': r_max, 'er_max': er_max, 'oer_max': oer_max}
+    def get_data(self,data: Union[AtomicData, ase.Atoms, str],pbc:Union[bool,list]=None, device: Union[str, torch.device]=None, AtomicData_options:dict=None):
         '''The function `get_data` takes input data in the form of a string, ase.Atoms object, or AtomicData
         object, processes it accordingly, and returns the AtomicData class.
         
@@ -79,15 +86,36 @@ def get_data(self,data: Union[AtomicData, ase.Atoms, str],AtomicData_options: di
             the loaded AtomicData object.
         
         '''
+        atomic_options = deepcopy(self.cutoffs)
+        if pbc is not None:
+            atomic_options.update({'pbc': pbc})
+
+        if AtomicData_options is not None:
+            if AtomicData_options.get('r_max', None) is not None:
+                if atomic_options['r_max'] != AtomicData_options.get('r_max'):
+                    atomic_options['r_max'] = AtomicData_options.get('r_max')
+                    log.warning(f'Overwrite the r_max setting in the model with the r_max setting in the AtomicData_options: {AtomicData_options.get("r_max")}')
+                    log.warning(f'This is very dangerous, please make sure you know what you are doing.')
+            if AtomicData_options.get('er_max', None) is not None:
+                if atomic_options['er_max'] != AtomicData_options.get('er_max'):
+                    atomic_options['er_max'] = AtomicData_options.get('er_max')
+                    log.warning(f'Overwrite the er_max setting in the model with the er_max setting in the AtomicData_options: {AtomicData_options.get("er_max")}')
+                    log.warning(f'This is very dangerous, please make sure you know what you are doing.')
+            if AtomicData_options.get('oer_max', None) is not None:
+                if atomic_options['oer_max'] != AtomicData_options.get('oer_max'):
+                    atomic_options['oer_max'] = AtomicData_options.get('oer_max')
+                    log.warning(f'Overwrite the oer_max setting in the model with the oer_max setting in the AtomicData_options: {AtomicData_options.get("oer_max")}')
+                    log.warning(f'This is very dangerous, please make sure you know what you are doing.')
 
         if isinstance(data, str):
             structase = read(data)
-            data = AtomicData.from_ase(structase, **AtomicData_options)
+            data = AtomicData.from_ase(structase, **atomic_options)
         elif isinstance(data, ase.Atoms):
             structase = data
-            data = AtomicData.from_ase(structase, **AtomicData_options)
+            data = AtomicData.from_ase(structase, **atomic_options)
         elif isinstance(data, AtomicData):
             # structase = data.to("cpu").to_ase()
+            log.info('The data is already an instance of AtomicData. Then the data is used directly.')
             data = data
         else:
             raise ValueError('data should be either a string, ase.Atoms, or AtomicData')
@@ -100,7 +128,7 @@ def get_data(self,data: Union[AtomicData, ase.Atoms, str],AtomicData_options: di
         return data
 
 
-    def get_eigs(self, data: Union[AtomicData, ase.Atoms, str], klist: np.ndarray, AtomicData_options: dict={}):
+    def get_eigs(self, data: Union[AtomicData, ase.Atoms, str], klist: np.ndarray, pbc:Union[bool,list]=None, AtomicData_options:dict=None):
         '''This function calculates eigenvalues for Hk at specified k-points.
         
         Parameters
@@ -120,7 +148,7 @@ def get_eigs(self, data: Union[AtomicData, ase.Atoms, str], klist: np.ndarray, A
         
         '''
             
-        data  = self.get_data(data=data, AtomicData_options=AtomicData_options, device=self.device)
+        data  = self.get_data(data=data, pbc=pbc, device=self.device,AtomicData_options=AtomicData_options)
         # set the kpoint of the AtomicData
         data[AtomicDataDict.KPOINT_KEY] = \
             torch.nested.as_nested_tensor([torch.as_tensor(klist, dtype=self.model.dtype, device=self.device)])
@@ -133,7 +161,7 @@ def get_eigs(self, data: Union[AtomicData, ase.Atoms, str], klist: np.ndarray, A
         return data, data[AtomicDataDict.ENERGY_EIGENVALUE_KEY][0].detach().cpu().numpy()
 
     def get_fermi_level(self, data: Union[AtomicData, ase.Atoms, str], nel_atom: dict, \
-                        meshgrid: list = None, klist: np.ndarray=None, AtomicData_options: dict={}):
+                        meshgrid: list = None, klist: np.ndarray=None, pbc:Union[bool,list]=None,AtomicData_options:dict=None):
         '''This function calculates the Fermi level based on provided data with iteration method, electron counts per atom, and
         optional parameters like specific k-points and eigenvalues.
         
@@ -184,7 +212,7 @@ def get_fermi_level(self, data: Union[AtomicData, ase.Atoms, str], nel_atom: dic
 
         # eigenvalues would be used if provided, otherwise the eigenvalues would be calculated from the model on the specified k-points
         if not AtomicDataDict.ENERGY_EIGENVALUE_KEY in data:
-            data, eigs = self.get_eigs(data=data, klist=klist, AtomicData_options=AtomicData_options) 
+            data, eigs = self.get_eigs(data=data, klist=klist, pbc=pbc, AtomicData_options=AtomicData_options) 
             log.info('Getting eigenvalues from the model.')
         else:
             log.info('The eigenvalues are already in data. will use them.')
diff --git a/dptb/tests/data/Sn/soc/dataset/set.0/info.json b/dptb/tests/data/Sn/soc/dataset/set.0/info.json
index 01857942..7086f714 100644
--- a/dptb/tests/data/Sn/soc/dataset/set.0/info.json
+++ b/dptb/tests/data/Sn/soc/dataset/set.0/info.json
@@ -2,12 +2,7 @@
     "nframes": 1,
     "natoms": -1,
     "pos_type": "ase",
-    "AtomicData_options": {
-        "r_max": 6.0,
-        "er_max": 5.0,
-        "oer_max":3.0,
-        "pbc": true
-    },
+    "pbc": true,
     "bandinfo": {
         "band_min": 0,
         "band_max":16,
diff --git a/dptb/tests/data/Sn/soc/input/input_soc.json b/dptb/tests/data/Sn/soc/input/input_soc.json
index fb18805d..86007a2f 100644
--- a/dptb/tests/data/Sn/soc/input/input_soc.json
+++ b/dptb/tests/data/Sn/soc/input/input_soc.json
@@ -43,7 +43,7 @@
     },
     "model_options": {
         "nnsk": {
-            "onsite": {"method": "strain","rs":6.0, "w": 0.1},
+            "onsite": {"method": "strain","rs":3.0, "w": 0.1},
             "hopping": {"method": "powerlaw", "rs":6.0, "w": 0.1},
             "soc":{"method":"uniform"},
             "push": false,
diff --git a/dptb/tests/data/hBN/dataset/kpath.0/info.json b/dptb/tests/data/hBN/dataset/kpath.0/info.json
index ac146605..41e9d706 100644
--- a/dptb/tests/data/hBN/dataset/kpath.0/info.json
+++ b/dptb/tests/data/hBN/dataset/kpath.0/info.json
@@ -2,12 +2,7 @@
     "nframes": 1,
     "natoms": 2,
     "pos_type": "ase",
-    "AtomicData_options": {
-        "r_max": 2.6,
-        "er_max": 2.6,
-        "oer_max":1.6,
-        "pbc": true
-    },
+    "pbc": true,
     "bandinfo": {
         "band_min": 0,
         "band_max": 6,
diff --git a/dptb/tests/data/hBN/input/input_mix_dftbsk.json b/dptb/tests/data/hBN/input/input_mix_dftbsk.json
index 5d214e0d..f9cbe2d4 100644
--- a/dptb/tests/data/hBN/input/input_mix_dftbsk.json
+++ b/dptb/tests/data/hBN/input/input_mix_dftbsk.json
@@ -29,7 +29,8 @@
     },
     "model_options": {
         "dftbsk": {
-            "skdata":"./examples/hBN_dftb/slakos"
+            "skdata":"./examples/hBN_dftb/slakos",
+            "r_max": 5.0
         },
         "embedding":{
             "method": "se2",
diff --git a/dptb/tests/data/test_sktb/dataset/kpath_spk.0/info.json b/dptb/tests/data/test_sktb/dataset/kpath_spk.0/info.json
index 4304669a..8912eaf7 100644
--- a/dptb/tests/data/test_sktb/dataset/kpath_spk.0/info.json
+++ b/dptb/tests/data/test_sktb/dataset/kpath_spk.0/info.json
@@ -2,12 +2,7 @@
     "nframes": 1,
     "natoms": 2,
     "pos_type": "ase",
-    "AtomicData_options": {
-        "r_max": 5.0,
-        "er_max": 5.0,
-        "oer_max": 2.5,
-        "pbc": true
-    },
+    "pbc": true,
     "bandinfo": {
         "band_min": 0,
         "band_max": 6,
diff --git a/dptb/tests/data/test_sktb/dataset/kpathmd25.0/info.json b/dptb/tests/data/test_sktb/dataset/kpathmd25.0/info.json
index 6485de04..823b0eba 100644
--- a/dptb/tests/data/test_sktb/dataset/kpathmd25.0/info.json
+++ b/dptb/tests/data/test_sktb/dataset/kpathmd25.0/info.json
@@ -2,12 +2,7 @@
     "nframes": 10,
     "natoms": 8,
     "pos_type": "ase",
-    "AtomicData_options": {
-        "r_max": 5.0,
-        "er_max": 5.0,
-        "oer_max": 2.5,
-        "pbc": true
-    },
+    "pbc": true,
     "bandinfo": {
         "band_min": 0,
         "band_max": 8,
diff --git a/dptb/tests/data/test_sktb/input/input_push_rs.json b/dptb/tests/data/test_sktb/input/input_push_rs.json
index 40d8b6f1..7dacbe85 100644
--- a/dptb/tests/data/test_sktb/input/input_push_rs.json
+++ b/dptb/tests/data/test_sktb/input/input_push_rs.json
@@ -33,6 +33,8 @@
         }
     },
     "data_options": {
+        "r_max": 5.0,
+        "oer_max":2.5,
         "train": {
             "root": "./dptb/tests/data/test_sktb/dataset/",
             "prefix": "kpath_spk",
diff --git a/dptb/tests/data/test_sktb/input/input_push_w.json b/dptb/tests/data/test_sktb/input/input_push_w.json
index 77e56525..997d0924 100644
--- a/dptb/tests/data/test_sktb/input/input_push_w.json
+++ b/dptb/tests/data/test_sktb/input/input_push_w.json
@@ -33,6 +33,8 @@
         }
     },
     "data_options": {
+        "r_max": 5.0,
+        "oer_max":2.5,
         "train": {
             "root": "./dptb/tests/data/test_sktb/dataset/",
             "prefix": "kpath_spk",
diff --git a/dptb/tests/test_SKHamiltonian.py b/dptb/tests/test_SKHamiltonian.py
index 2ef4153e..40607943 100644
--- a/dptb/tests/test_SKHamiltonian.py
+++ b/dptb/tests/test_SKHamiltonian.py
@@ -39,6 +39,9 @@ class TestSKHamiltonian:
         "push": None}
     }
     data_options = {
+        "r_max": 2.6,
+        "er_max": 2.6,
+        "oer_max":1.6,
         "train": {
             "root": f"{rootdir}/hBN/dataset",
             "prefix": "kpath",
@@ -46,7 +49,7 @@ class TestSKHamiltonian:
         }
     }
 
-    train_datasets = build_dataset(**data_options["train"], **common_options)
+    train_datasets = build_dataset(**data_options, **data_options["train"], **common_options)
     train_loader = DataLoader(dataset=train_datasets, batch_size=1, shuffle=True)
 
     batch = next(iter(train_loader))
diff --git a/dptb/tests/test_block_to_feature.py b/dptb/tests/test_block_to_feature.py
index db1b0d53..86880339 100644
--- a/dptb/tests/test_block_to_feature.py
+++ b/dptb/tests/test_block_to_feature.py
@@ -40,6 +40,9 @@ class TestBlock2Feature:
         "push": None}
     }
     data_options = {
+        "r_max": 2.6,
+        "er_max": 2.6,
+        "oer_max":1.6,
         "train": {
             "root": f"{rootdir}/hBN/dataset",
             "prefix": "kpath",
@@ -47,7 +50,7 @@ class TestBlock2Feature:
         }
     }
 
-    train_datasets = build_dataset(**data_options["train"], **common_options)
+    train_datasets = build_dataset(**data_options, **data_options["train"], **common_options)
     train_loader = DataLoader(dataset=train_datasets, batch_size=1, shuffle=True)
 
     batch = next(iter(train_loader))
diff --git a/dptb/tests/test_build_dataset.py b/dptb/tests/test_build_dataset.py
index d10a9c9f..ca1f3ed9 100644
--- a/dptb/tests/test_build_dataset.py
+++ b/dptb/tests/test_build_dataset.py
@@ -10,6 +10,9 @@ def root_directory(request):
 
 def test_build_dataset_success(root_directory):
     set_options = {
+        "r_max": 5.0,
+        "er_max": 5.0,
+        "oer_max": 2.5,
         "root": f"{root_directory}/dptb/tests/data/test_sktb/dataset",
         "prefix": "kpath_spk",
         "get_eigenvalues": True,
@@ -53,6 +56,9 @@ def test_build_dataset_success(root_directory):
 
 def test_build_dataset_fail(root_directory):
     set_options = {
+        "r_max": 5.0,
+        "er_max": 5.0,
+        "oer_max": 2.5,
         "root": f"{root_directory}/dptb/tests/data/test_sktb/dataset",
         "prefix": "kpath_spk",
         "get_eigenvalues": False,
diff --git a/dptb/tests/test_dataloader_batch.py b/dptb/tests/test_dataloader_batch.py
index b96dda0b..329ed7ee 100644
--- a/dptb/tests/test_dataloader_batch.py
+++ b/dptb/tests/test_dataloader_batch.py
@@ -14,6 +14,9 @@
 
 class TestDataLoaderBatch:
     data_options = {
+        "r_max": 5.0,
+        "er_max": 5.0,
+        "oer_max": 2.5,
         "train": {
             "root": f"{rootdir}/test_sktb/dataset",
             "prefix": "kpath_spk",
@@ -29,7 +32,7 @@ class TestDataLoaderBatch:
     "overlap": False,
     "seed": 3982377700
     }
-    train_datasets = build_dataset(**data_options["train"], **common_options)
+    train_datasets = build_dataset(**data_options, **data_options["train"], **common_options)
 
     def test_init(self):
         train_loader = DataLoader(dataset=self.train_datasets, batch_size=1, shuffle=True)
diff --git a/dptb/tests/test_default_dataset.py b/dptb/tests/test_default_dataset.py
index 590ad9be..6f585218 100644
--- a/dptb/tests/test_default_dataset.py
+++ b/dptb/tests/test_default_dataset.py
@@ -17,10 +17,10 @@ class TestDefaultDatasetSKTB:
     info_files = {'kpath_spk.0': {'nframes': 1,
       'natoms': 2,
       'pos_type': 'ase',
-      'AtomicData_options': {'r_max': 5.0,
+      'pbc': True,
+      'r_max': 5.0,
        'er_max': 5.0,
        'oer_max': 2.5,
-       'pbc': True},
       'bandinfo': {'nkpoints': 61,
        'nbands': 14,
        'band_min': 0,
@@ -56,7 +56,7 @@ def test_inparas(self):
     def test_raw_data(self):
         assert len(self.dataset.raw_data) == 1
         assert isinstance(self.dataset.raw_data[0], _TrajData)
-        assert self.dataset.raw_data[0].AtomicData_options == {'r_max': 5.0, 'er_max': 5.0, 'oer_max': 2.5, 'pbc': True}
+        # assert self.dataset.raw_data[0].AtomicData_options == {'r_max': 5.0, 'er_max': 5.0, 'oer_max': 2.5, 'pbc': True}
         assert self.dataset.raw_data[0].info == self.info_files['kpath_spk.0']
         assert "bandinfo" in self.dataset.raw_data[0].info
         assert  list(self.dataset.raw_data[0].data.keys()) == (['cell', 'pos', 'atomic_numbers', 'kpoint', 'eigenvalue'])
diff --git a/dptb/tests/test_dftbsk.py b/dptb/tests/test_dftbsk.py
index 2ae5ba9c..9619d85c 100644
--- a/dptb/tests/test_dftbsk.py
+++ b/dptb/tests/test_dftbsk.py
@@ -31,13 +31,16 @@ class TestDFTBSK:
         }
     }
     data_options = {
+        "r_max": 2.6,
+        "er_max": 2.6,
+        "oer_max":1.6,
         "train": {
             "root": f"{rootdir}/hBN/dataset",
             "prefix": "kpath",
             "get_eigenvalues": False
         }
     }
-    train_datasets = build_dataset(**data_options["train"], **common_options)
+    train_datasets = build_dataset(**data_options, **data_options["train"], **common_options)
     train_loader = DataLoader(dataset=train_datasets, batch_size=1, shuffle=True)
 
     batch = next(iter(train_loader))
diff --git a/dptb/tests/test_from_v1json.py b/dptb/tests/test_from_v1json.py
index f7f3e6ce..d0a87d6f 100644
--- a/dptb/tests/test_from_v1json.py
+++ b/dptb/tests/test_from_v1json.py
@@ -66,27 +66,93 @@ def test_bands(self):
             device=model.device)
 
         stru_data = f"{rootdir}/json_model/AlAs.vasp"
-        AtomicData_options = {"r_max": 5.2, "pbc": True}
-
         eigenstatus = bcal.get_bands(data=stru_data, 
-               kpath_kwargs=kpath_kwargs, 
-               AtomicData_options=AtomicData_options)
+               kpath_kwargs=kpath_kwargs)
         
-        expected_bands =np.array([[-2.48727150e+01, -1.29382324e+01, -1.29382257e+01, -1.29382229e+01, -1.10868120e+01, -8.07862854e+00, -8.07862568e+00, -8.07861805e+00,  9.56408596e+00,  9.56408691e+00,  1.25271873e+01,  1.25271950e+01,  1.25271978e+01,  4.23655891e+01,  4.23656044e+01,  4.32170753e+01,  4.32170792e+01,  4.32170868e+01],
-             [-2.41187267e+01, -1.61148472e+01, -1.42793083e+01, -1.42793045e+01, -1.03604565e+01, -8.68612957e+00, -5.90628624e+00, -5.90628576e+00,  2.25617599e+00,  5.51729870e+00,  5.51730347e+00,  5.61441135e+00,  5.90860081e+00,  2.50449829e+01,  2.82622643e+01,  2.82622776e+01,  2.84239502e+01,  3.07470131e+01],
-             [-2.29336300e+01, -1.85238571e+01, -1.51972685e+01, -1.51972666e+01, -1.13513584e+01, -1.05228834e+01, -2.21334386e+00, -2.21334243e+00, -3.03742558e-01, -3.03741843e-01, -9.65526607e-03,  8.24528575e-01,  1.84810734e+00,  7.89270067e+00,  1.01749058e+01,  1.01749077e+01,  1.34912348e+01,  1.40874834e+01],
-             [-2.29474239e+01, -1.84172096e+01, -1.56978197e+01, -1.50829716e+01, -1.10063257e+01, -9.69069576e+00, -2.91590619e+00, -2.64113235e+00, -1.43450952e+00, -4.38025206e-01,  1.01333761e+00,  1.07858098e+00,  3.61593747e+00,  7.17037296e+00,  9.29849529e+00,  1.00337200e+01,  1.38197346e+01,  1.42732258e+01],
-             [-2.30109138e+01, -1.81435585e+01, -1.63736401e+01, -1.47889500e+01, -1.06536665e+01, -7.59100485e+00, -4.40897274e+00, -4.01978016e+00, -1.59141457e+00, -8.14805627e-02,  1.07713044e+00,  2.36757493e+00,  6.39950705e+00,  6.44096851e+00,  8.05662537e+00,  1.10570469e+01,  1.42302742e+01,  1.53123789e+01],
-             [-2.30108986e+01, -1.81435699e+01, -1.63736362e+01, -1.47889528e+01, -1.06536646e+01, -7.59101057e+00, -4.40896845e+00, -4.01978016e+00, -1.59141552e+00, -8.14811662e-02,  1.07712996e+00,  2.36757469e+00,  6.39950800e+00,  6.44096851e+00,  8.05662632e+00,  1.10570469e+01,  1.42302704e+01,  1.53123856e+01],
-             [-2.40611782e+01, -1.67647114e+01, -1.50329933e+01, -1.34557276e+01, -9.01750469e+00, -7.44570971e+00, -7.16721439e+00, -6.34023905e+00,  2.99699736e+00,  3.46649384e+00,  4.46376228e+00,  5.20905399e+00,  7.82006931e+00,  2.53436356e+01,  2.59452019e+01,  2.75783978e+01,  2.84669800e+01,  2.92158451e+01],
-             [-2.48727150e+01, -1.29382324e+01, -1.29382257e+01, -1.29382229e+01, -1.10868120e+01, -8.07862854e+00, -8.07862568e+00, -8.07861805e+00,  9.56408596e+00,  9.56408691e+00,  1.25271873e+01,  1.25271950e+01,  1.25271978e+01,  4.23655891e+01,  4.23656044e+01,  4.32170753e+01,  4.32170792e+01,  4.32170868e+01],
-             [-2.43790150e+01, -1.64551792e+01, -1.34435387e+01, -1.34435349e+01, -1.03514795e+01, -7.39460945e+00, -7.39460516e+00, -6.10483932e+00,  4.67000580e+00,  4.67000914e+00,  6.74771929e+00,  6.74772310e+00,  9.39733410e+00,  3.10563354e+01,  3.10563450e+01,  3.17371826e+01,  3.17371864e+01,  3.35946846e+01],
-             [-2.35396881e+01, -1.85059109e+01, -1.37993116e+01, -1.37993116e+01, -1.08380241e+01, -7.56033421e+00, -7.56033087e+00, -3.32421374e+00, -4.98459250e-01, -4.98458147e-01,  4.68962049e+00,  4.68962288e+00,  7.64235640e+00,  1.68248940e+01,  1.68248997e+01,  2.04327011e+01,  2.04327030e+01,  2.12005653e+01],
-             [-2.31961079e+01, -1.82634125e+01, -1.56346197e+01, -1.44923830e+01, -9.23417282e+00, -7.92826271e+00, -5.95008469e+00, -4.99026012e+00, -1.32351279e+00, -5.01590669e-01,  2.95195317e+00,  4.62497950e+00,  5.44099808e+00,  1.17575951e+01,  1.19310246e+01,  1.50337820e+01,  1.79441051e+01,  1.80985184e+01],
-             [-2.29424934e+01, -1.80575047e+01, -1.62370167e+01, -1.56745434e+01, -8.23033428e+00, -7.16741085e+00, -6.62496185e+00, -5.73856449e+00, -1.48688376e+00,  1.80971527e+00,  2.45554900e+00,  3.85232139e+00,  4.23087120e+00,  5.92445564e+00,  6.44421244e+00,  8.21325207e+00,  1.44543571e+01,  1.44987440e+01],
-             [-2.29404392e+01, -1.83383312e+01, -1.57138681e+01, -1.54623451e+01, -1.01436739e+01, -9.37874889e+00, -4.06893778e+00, -3.25271797e+00, -1.23538244e+00, -4.17988628e-01,  1.19791162e+00,  2.69611549e+00,  3.94141436e+00,  6.43033361e+00,  8.37113857e+00,  9.67157173e+00,  1.41308174e+01,  1.42368813e+01],
-             [-2.29336300e+01, -1.85238571e+01, -1.51972685e+01, -1.51972666e+01, -1.13513584e+01, -1.05228834e+01, -2.21334386e+00, -2.21334243e+00, -3.03742558e-01, -3.03741843e-01, -9.65526607e-03,  8.24528575e-01,  1.84810734e+00,  7.89270067e+00,  1.01749058e+01,  1.01749077e+01,  1.34912348e+01,  1.40874834e+01]])
-
+        expected_bands =np.array([[-2.48738842e+01, -1.29387579e+01, -1.29387531e+01,
+        -1.29387484e+01, -1.10866852e+01, -8.07882595e+00,
+        -8.07881832e+00, -8.07881737e+00,  9.56978989e+00,
+         9.56979179e+00,  1.25314865e+01,  1.25314980e+01,
+         1.25315008e+01,  4.23717499e+01,  4.23717537e+01,
+         4.32215462e+01,  4.32215538e+01,  4.32215614e+01],
+       [-2.41191730e+01, -1.61150684e+01, -1.42791767e+01,
+        -1.42791719e+01, -1.03606348e+01, -8.68642616e+00,
+        -5.90601444e+00, -5.90600967e+00,  2.25788760e+00,
+         5.51916361e+00,  5.51916647e+00,  5.61485243e+00,
+         5.91050625e+00,  2.50469570e+01,  2.82641335e+01,
+         2.82641392e+01,  2.84246483e+01,  3.07487679e+01],
+       [-2.29339600e+01, -1.85236092e+01, -1.51975479e+01,
+        -1.51975431e+01, -1.13508320e+01, -1.05211630e+01,
+        -2.21044850e+00, -2.21044683e+00, -3.01877409e-01,
+        -3.01874220e-01, -5.50282327e-03,  8.32842171e-01,
+         1.85165548e+00,  7.89621782e+00,  1.01784697e+01,
+         1.01784744e+01,  1.34970484e+01,  1.40907288e+01],
+       [-2.29477081e+01, -1.84168339e+01, -1.56979408e+01,
+        -1.50834084e+01, -1.10056238e+01, -9.68981457e+00,
+        -2.91448879e+00, -2.63903880e+00, -1.43107760e+00,
+        -4.35178548e-01,  1.01621652e+00,  1.08422828e+00,
+         3.61865139e+00,  7.17336273e+00,  9.30155849e+00,
+         1.00361338e+01,  1.38237772e+01,  1.42762747e+01],
+       [-2.30109749e+01, -1.81430511e+01, -1.63737125e+01,
+        -1.47896776e+01, -1.06530704e+01, -7.59097767e+00,
+        -4.40895557e+00, -4.01798630e+00, -1.59009695e+00,
+        -8.00317004e-02,  1.07963777e+00,  2.36884308e+00,
+         6.40078640e+00,  6.44294930e+00,  8.05769444e+00,
+         1.10569324e+01,  1.42315102e+01,  1.53139381e+01],
+       [-2.30109730e+01, -1.81430492e+01, -1.63737125e+01,
+        -1.47896767e+01, -1.06530619e+01, -7.59097576e+00,
+        -4.40895939e+00, -4.01798677e+00, -1.59010005e+00,
+        -8.00331011e-02,  1.07963753e+00,  2.36884212e+00,
+         6.40078783e+00,  6.44294262e+00,  8.05769157e+00,
+         1.10569296e+01,  1.42315063e+01,  1.53139343e+01],
+       [-2.40614185e+01, -1.67648468e+01, -1.50328369e+01,
+        -1.34554472e+01, -9.01719761e+00, -7.44597864e+00,
+        -7.16829062e+00, -6.34035254e+00,  2.99600887e+00,
+         3.46569014e+00,  4.46293497e+00,  5.20906973e+00,
+         7.81929064e+00,  2.53426857e+01,  2.59447289e+01,
+         2.75785408e+01,  2.84667950e+01,  2.92152786e+01],
+       [-2.48738842e+01, -1.29387579e+01, -1.29387531e+01,
+        -1.29387484e+01, -1.10866852e+01, -8.07882595e+00,
+        -8.07881832e+00, -8.07881737e+00,  9.56978989e+00,
+         9.56979179e+00,  1.25314865e+01,  1.25314980e+01,
+         1.25315008e+01,  4.23717499e+01,  4.23717537e+01,
+         4.32215462e+01,  4.32215538e+01,  4.32215614e+01],
+       [-2.43795700e+01, -1.64551907e+01, -1.34434357e+01,
+        -1.34434280e+01, -1.03514872e+01, -7.39513445e+00,
+        -7.39513063e+00, -6.10492849e+00,  4.66958141e+00,
+         4.66959238e+00,  6.74782705e+00,  6.74782896e+00,
+         9.39744949e+00,  3.10566292e+01,  3.10566330e+01,
+         3.17376080e+01,  3.17376156e+01,  3.35952110e+01],
+       [-2.35389977e+01, -1.85056343e+01, -1.37990303e+01,
+        -1.37990227e+01, -1.08380346e+01, -7.56264687e+00,
+        -7.56264400e+00, -3.32416415e+00, -5.02199292e-01,
+        -5.02196670e-01,  4.68519068e+00,  4.68519211e+00,
+         7.63819790e+00,  1.68208370e+01,  1.68208504e+01,
+         2.04270325e+01,  2.04270401e+01,  2.11967201e+01],
+       [-2.31957893e+01, -1.82630310e+01, -1.56345882e+01,
+        -1.44928350e+01, -9.23434162e+00, -7.92852402e+00,
+        -5.95022917e+00, -4.99064732e+00, -1.32462871e+00,
+        -5.02915382e-01,  2.95047784e+00,  4.62426805e+00,
+         5.43929529e+00,  1.17566347e+01,  1.19293985e+01,
+         1.50316639e+01,  1.79418583e+01,  1.80968723e+01],
+       [-2.29426270e+01, -1.80574532e+01, -1.62369022e+01,
+        -1.56748543e+01, -8.22931576e+00, -7.16701651e+00,
+        -6.62407303e+00, -5.73891783e+00, -1.48577607e+00,
+         1.81063569e+00,  2.45844960e+00,  3.85335517e+00,
+         4.23286629e+00,  5.92695141e+00,  6.44531107e+00,
+         8.21370125e+00,  1.44566507e+01,  1.44983978e+01],
+       [-2.29406662e+01, -1.83379650e+01, -1.57140999e+01,
+        -1.54625340e+01, -1.01433325e+01, -9.37792110e+00,
+        -4.06694746e+00, -3.25191188e+00, -1.23258555e+00,
+        -4.15170372e-01,  1.20202124e+00,  2.69886231e+00,
+         3.94393396e+00,  6.43281651e+00,  8.37365723e+00,
+         9.67361927e+00,  1.41347656e+01,  1.42384768e+01],
+       [-2.29339600e+01, -1.85236092e+01, -1.51975479e+01,
+        -1.51975431e+01, -1.13508320e+01, -1.05211630e+01,
+        -2.21044850e+00, -2.21044683e+00, -3.01877409e-01,
+        -3.01874220e-01, -5.50282327e-03,  8.32842171e-01,
+         1.85165548e+00,  7.89621782e+00,  1.01784697e+01,
+         1.01784744e+01,  1.34970484e+01,  1.40907288e+01]], dtype=np.float32)
         assert np.allclose(eigenstatus["eigenvalues"], expected_bands, atol=1e-4)
 
 
@@ -149,25 +215,77 @@ def test_bands(self):
             device=model.device)
 
         stru_data = f"{rootdir}/json_model/silicon.vasp"
-        AtomicData_options = {"r_max": 2.6, "oer_max":2.5, "pbc": True}
 
         eigenstatus = bcal.get_bands(data=stru_data, 
-               kpath_kwargs=kpath_kwargs, 
-               AtomicData_options=AtomicData_options)
-        
-        expected_bands =np.array([[-20.259584  ,  -8.328452  ,  -8.328452  ,  -8.328451  ,  -5.782879  ,  -5.782879  ,  -5.7828774 ,  -4.800206  ,  -0.8470682 ,  -0.8470663 ,   4.9619126 ,   4.961913  ,   4.9619136 ,   6.4527135 ,   6.452714  ,   6.452715  ,  10.1427765 ,  10.142781  ],
-                                  [-19.173727  , -11.876228  , -10.340221  , -10.34022   ,  -6.861969  ,  -4.9920564 ,  -2.1901789 ,  -2.1901765 ,  -0.9258757 ,   0.76235735,   4.2745295 ,   4.2745323 ,   4.990632  ,   5.55916   ,   5.559161  ,   8.533346  ,   8.716906  ,  11.661528  ],
-                                  [-16.172304  , -16.172298  , -11.271987  , -11.271983  ,  -7.4252186 ,  -7.4252176 ,   2.1354833 ,   2.135485  ,   2.4157436 ,   2.4157462 ,   2.7901921 ,   2.7901928 ,   3.6496053 ,   3.649607  ,   4.6478515 ,   4.6478524 ,  11.951376  ,  11.951382  ],
-                                  [-16.322428  , -15.988458  , -11.912281  , -11.193047  ,  -7.3037252 ,  -6.193884  ,   1.205529  ,   1.386399  ,   1.6548665 ,   1.8747401 ,   2.580269  ,   3.005812  ,   3.4153423 ,   4.022218  ,   5.4699235 ,   6.23605   ,  11.671546  ,  11.832637  ],
-                                  [-16.799667  , -15.46194   , -12.612725  , -10.942198  ,  -6.9641047 ,  -3.7625234 ,  -0.7360446 ,   0.28918347,   0.47772366,   0.6291326 ,   2.4882295 ,   3.1617444 ,   4.0417986 ,   4.6302714 ,   6.5749364 ,   8.062847  ,  10.855666  ,  11.509191  ],
-                                  [-16.799667  , -15.461945  , -12.612727  , -10.9422035 ,  -6.9641085 ,  -3.7625222 ,  -0.73604566,   0.28918162,   0.4777242 ,   0.62913096,   2.4882276 ,   3.1617427 ,   4.041798  ,   4.6302724 ,   6.5749335 ,   8.062847  ,  10.855668  ,  11.509187  ],
-                                  [-19.12568   , -12.3842125 , -11.161121  ,  -9.196095  ,  -5.6751695 ,  -4.8814125 ,  -3.031833  ,  -2.0943422 ,  -2.0460339 ,   0.7482071 ,   3.5014281 ,   4.8715053 ,   5.2672033 ,   5.640518  ,   6.8847284 ,   7.1940207 ,  10.2244625 ,  10.705325  ],
-                                  [-20.259584  ,  -8.328452  ,  -8.328452  ,  -8.328451  ,  -5.782879  ,  -5.782879  ,  -5.7828774 ,  -4.800206  ,  -0.8470682 ,  -0.8470663 ,   4.9619126 ,   4.961913  ,   4.9619136 ,   6.4527135 ,   6.452714  ,   6.452715  ,  10.1427765 ,  10.142781  ],
-                                  [-19.503462  , -12.068741  ,  -9.1723    ,  -9.172297  ,  -6.1124167 ,  -4.959279  ,  -4.959278  ,  -1.1632957 ,  -1.1632944 ,  -1.1617142 ,   4.8985996 ,   5.257441  ,   5.257443  ,   6.191231  ,   6.2036867 ,   6.203688  ,  10.432747  ,  10.432751  ],
-                                  [-18.410772  , -14.457038  ,  -9.623036  ,  -9.623032  ,  -6.8522253 ,  -5.3134403 ,  -5.3134394 ,   0.34697238,   0.3469742 ,   1.5420008 ,   3.4220562 ,   3.4220574 ,   5.17151   ,   5.250026  ,   7.019237  ,   7.0192394 ,  10.747205  ,  10.747212  ],
-                                  [-17.752392  , -14.654745  , -11.930272  , -10.688241  ,  -5.6049733 ,  -4.517258  ,  -2.4019077 ,  -0.54922515,  -0.42735893,   1.6003915 ,   2.3744426 ,   3.288959  ,   4.6278877 ,   4.90705   ,   7.08742   ,   9.220286  ,   9.723419  ,  11.138031  ],
-                                  [-16.101318  , -16.101318  , -12.243194  , -12.243191  ,  -3.945867  ,  -3.9458647 ,  -2.42533   ,  -2.4253287 ,   2.3399496 ,   2.3399508 ,   2.8937058 ,   2.893708  ,   3.2351081 ,   3.235109  ,   7.9230847 ,   7.9230857 ,  11.04461   ,  11.044615  ],
-                                  [-16.138231  , -16.138226  , -11.826924  , -11.826924  ,  -6.087353  ,  -6.087353  ,   0.08484415,   0.08484493,   2.342462  ,   2.3424625 ,   2.8806267 ,   2.8806279 ,   3.2753062 ,   3.2753084 ,   6.610969  ,   6.6109715 ,  11.579055  ,  11.579056  ],
-                                  [-16.172304  , -16.172298  , -11.271987  , -11.271983  ,  -7.4252186 ,  -7.4252176 ,   2.1354833 ,   2.135485  ,   2.4157436 ,   2.4157462 ,   2.7901921 ,   2.7901928 ,   3.6496053 ,   3.649607  ,   4.6478515 ,   4.6478524 ,  11.951376  ,  11.951382  ]])
-
+               kpath_kwargs=kpath_kwargs)
+        expected_bands = np.array([[-28.032394  , -12.518021  ,  -8.789028  ,  -8.789027  ,
+                                     -8.78902   ,  -6.074078  ,  -6.074069  ,  -6.0740604 ,
+                                     17.192019  ,  17.192028  ,  22.030336  ,  22.030338  ,
+                                     22.03035   ,  23.343376  ,  23.343376  ,  23.343384  ,
+                                     28.18668   ,  28.186697  ],
+                                   [-26.710665  , -17.258825  , -11.786415  , -11.786402  ,
+                                     -6.316819  ,  -6.08972   ,  -2.2474113 ,  -2.2474105 ,
+                                     15.599638  ,  18.773561  ,  20.637032  ,  21.751331  ,
+                                     21.751333  ,  22.788795  ,  22.788813  ,  26.043669  ,
+                                     26.558607  ,  29.842487  ],
+                                   [-22.908417  , -22.90841   , -13.267318  , -13.267316  ,
+                                     -5.855864  ,  -5.8558598 ,   0.13847035,   0.13847637,
+                                     17.0159    ,  17.0159    ,  21.383863  ,  21.383865  ,
+                                     22.246996  ,  22.247007  ,  22.64281   ,  22.642822  ,
+                                     29.825714  ,  29.825722  ],
+                                   [-23.125595  , -22.677975  , -13.552594  , -13.126421  ,
+                                     -6.040592  ,  -5.239112  ,  -0.16200367,   0.15783598,
+                                     17.022974  ,  17.076164  ,  20.278925  ,  21.015097  ,
+                                     21.579382  ,  22.268646  ,  23.596603  ,  24.191101  ,
+                                     29.491728  ,  29.689163  ],
+                                   [-23.748362  , -21.997149  , -13.956201  , -12.712631  ,
+                                     -6.524768  ,  -3.9821868 ,  -0.98925126,   0.1548973 ,
+                                     17.227066  ,  17.242361  ,  18.778227  ,  20.15013   ,
+                                     22.017757  ,  22.306322  ,  24.653385  ,  25.92878   ,
+                                     28.555614  ,  29.325285  ],
+                                   [-23.748354  , -21.997162  , -13.956195  , -12.712616  ,
+                                     -6.5247726 ,  -3.9821932 ,  -0.98925066,   0.15489526,
+                                     17.22706   ,  17.242352  ,  18.778234  ,  20.150133  ,
+                                     22.017756  ,  22.306314  ,  24.653393  ,  25.928793  ,
+                                     28.555605  ,  29.325268  ],
+                                   [-26.629864  , -17.600563  , -12.377507  , -10.340946  ,
+                                     -8.252066  ,  -4.5056643 ,  -3.1107721 ,  -1.5953344 ,
+                                     16.209995  ,  17.483477  ,  20.519865  ,  21.010235  ,
+                                     22.459747  ,  22.790142  ,  24.399433  ,  24.6775    ,
+                                     28.152231  ,  28.817282  ],
+                                   [-28.032394  , -12.518021  ,  -8.789028  ,  -8.789027  ,
+                                     -8.78902   ,  -6.074078  ,  -6.074069  ,  -6.0740604 ,
+                                     17.192019  ,  17.192028  ,  22.030336  ,  22.030338  ,
+                                     22.03035   ,  23.343376  ,  23.343376  ,  23.343384  ,
+                                     28.18668   ,  28.186697  ],
+                                   [-27.095016  , -16.587624  , -10.282119  , -10.282109  ,
+                                     -9.392605  ,  -4.47474   ,  -4.4747334 ,  -2.0351746 ,
+                                     16.790812  ,  16.79082   ,  21.469982  ,  21.469984  ,
+                                     22.261318  ,  23.112501  ,  23.889814  ,  23.889832  ,
+                                     28.476255  ,  28.476261  ],
+                                   [-25.650513  , -19.811338  , -11.168917  , -11.168911  ,
+                                     -9.854099  ,  -3.526547  ,  -3.5265448 ,   0.52536994,
+                                     17.867775  ,  17.86778   ,  18.799477  ,  18.799482  ,
+                                     22.678572  ,  22.67859   ,  25.41526   ,  25.415262  ,
+                                     28.463633  ,  28.463642  ],
+                                   [-24.895058  , -20.650934  , -13.44941   , -12.120061  ,
+                                     -7.257396  ,  -3.5902717 ,  -2.117014  ,   0.15689819,
+                                     17.095425  ,  17.784517  ,  18.656044  ,  19.36713   ,
+                                     22.366032  ,  22.408947  ,  25.288326  ,  26.88247   ,
+                                     27.520784  ,  28.909798  ],
+                                   [-22.888115  , -22.888107  , -13.577753  , -13.577752  ,
+                                     -5.001314  ,  -5.0013084 ,  -0.38720337,  -0.38719827,
+                                     17.119455  ,  17.11947   ,  19.495407  ,  19.495422  ,
+                                     21.823872  ,  21.823875  ,  25.932095  ,  25.9321    ,
+                                     28.705719  ,  28.705719  ],
+                                   [-22.898304  , -22.898302  , -13.427348  , -13.427342  ,
+                                     -5.4427934 ,  -5.442793  ,  -0.10512064,  -0.10511683,
+                                     17.060398  ,  17.060402  ,  20.222359  ,  20.222364  ,
+                                     21.842312  ,  21.842323  ,  24.5921    ,  24.5921    ,
+                                     29.378563  ,  29.378567  ],
+                                   [-22.908417  , -22.90841   , -13.267318  , -13.267316  ,
+                                     -5.855864  ,  -5.8558598 ,   0.13847035,   0.13847637,
+                                     17.0159    ,  17.0159    ,  21.383863  ,  21.383865  ,
+                                     22.246996  ,  22.247007  ,  22.64281   ,  22.642822  ,
+                                     29.825714  ,  29.825722  ]])
         assert np.allclose(eigenstatus["eigenvalues"], expected_bands, atol=1e-4)
\ No newline at end of file
diff --git a/dptb/tests/test_from_v2json.py b/dptb/tests/test_from_v2json.py
index 172e04d7..b4dbd885 100644
--- a/dptb/tests/test_from_v2json.py
+++ b/dptb/tests/test_from_v2json.py
@@ -42,27 +42,94 @@ def test_bands(self):
             device=model.device)
 
         stru_data = f"{rootdir}/json_model/AlAs.vasp"
-        AtomicData_options = {"r_max": 5.2, "pbc": True}
 
         eigenstatus = bcal.get_bands(data=stru_data, 
-               kpath_kwargs=kpath_kwargs, 
-               AtomicData_options=AtomicData_options)
+               kpath_kwargs=kpath_kwargs)
         
-        expected_bands =np.array([[-2.48727150e+01, -1.29382324e+01, -1.29382257e+01, -1.29382229e+01, -1.10868120e+01, -8.07862854e+00, -8.07862568e+00, -8.07861805e+00,  9.56408596e+00,  9.56408691e+00,  1.25271873e+01,  1.25271950e+01,  1.25271978e+01,  4.23655891e+01,  4.23656044e+01,  4.32170753e+01,  4.32170792e+01,  4.32170868e+01],
-             [-2.41187267e+01, -1.61148472e+01, -1.42793083e+01, -1.42793045e+01, -1.03604565e+01, -8.68612957e+00, -5.90628624e+00, -5.90628576e+00,  2.25617599e+00,  5.51729870e+00,  5.51730347e+00,  5.61441135e+00,  5.90860081e+00,  2.50449829e+01,  2.82622643e+01,  2.82622776e+01,  2.84239502e+01,  3.07470131e+01],
-             [-2.29336300e+01, -1.85238571e+01, -1.51972685e+01, -1.51972666e+01, -1.13513584e+01, -1.05228834e+01, -2.21334386e+00, -2.21334243e+00, -3.03742558e-01, -3.03741843e-01, -9.65526607e-03,  8.24528575e-01,  1.84810734e+00,  7.89270067e+00,  1.01749058e+01,  1.01749077e+01,  1.34912348e+01,  1.40874834e+01],
-             [-2.29474239e+01, -1.84172096e+01, -1.56978197e+01, -1.50829716e+01, -1.10063257e+01, -9.69069576e+00, -2.91590619e+00, -2.64113235e+00, -1.43450952e+00, -4.38025206e-01,  1.01333761e+00,  1.07858098e+00,  3.61593747e+00,  7.17037296e+00,  9.29849529e+00,  1.00337200e+01,  1.38197346e+01,  1.42732258e+01],
-             [-2.30109138e+01, -1.81435585e+01, -1.63736401e+01, -1.47889500e+01, -1.06536665e+01, -7.59100485e+00, -4.40897274e+00, -4.01978016e+00, -1.59141457e+00, -8.14805627e-02,  1.07713044e+00,  2.36757493e+00,  6.39950705e+00,  6.44096851e+00,  8.05662537e+00,  1.10570469e+01,  1.42302742e+01,  1.53123789e+01],
-             [-2.30108986e+01, -1.81435699e+01, -1.63736362e+01, -1.47889528e+01, -1.06536646e+01, -7.59101057e+00, -4.40896845e+00, -4.01978016e+00, -1.59141552e+00, -8.14811662e-02,  1.07712996e+00,  2.36757469e+00,  6.39950800e+00,  6.44096851e+00,  8.05662632e+00,  1.10570469e+01,  1.42302704e+01,  1.53123856e+01],
-             [-2.40611782e+01, -1.67647114e+01, -1.50329933e+01, -1.34557276e+01, -9.01750469e+00, -7.44570971e+00, -7.16721439e+00, -6.34023905e+00,  2.99699736e+00,  3.46649384e+00,  4.46376228e+00,  5.20905399e+00,  7.82006931e+00,  2.53436356e+01,  2.59452019e+01,  2.75783978e+01,  2.84669800e+01,  2.92158451e+01],
-             [-2.48727150e+01, -1.29382324e+01, -1.29382257e+01, -1.29382229e+01, -1.10868120e+01, -8.07862854e+00, -8.07862568e+00, -8.07861805e+00,  9.56408596e+00,  9.56408691e+00,  1.25271873e+01,  1.25271950e+01,  1.25271978e+01,  4.23655891e+01,  4.23656044e+01,  4.32170753e+01,  4.32170792e+01,  4.32170868e+01],
-             [-2.43790150e+01, -1.64551792e+01, -1.34435387e+01, -1.34435349e+01, -1.03514795e+01, -7.39460945e+00, -7.39460516e+00, -6.10483932e+00,  4.67000580e+00,  4.67000914e+00,  6.74771929e+00,  6.74772310e+00,  9.39733410e+00,  3.10563354e+01,  3.10563450e+01,  3.17371826e+01,  3.17371864e+01,  3.35946846e+01],
-             [-2.35396881e+01, -1.85059109e+01, -1.37993116e+01, -1.37993116e+01, -1.08380241e+01, -7.56033421e+00, -7.56033087e+00, -3.32421374e+00, -4.98459250e-01, -4.98458147e-01,  4.68962049e+00,  4.68962288e+00,  7.64235640e+00,  1.68248940e+01,  1.68248997e+01,  2.04327011e+01,  2.04327030e+01,  2.12005653e+01],
-             [-2.31961079e+01, -1.82634125e+01, -1.56346197e+01, -1.44923830e+01, -9.23417282e+00, -7.92826271e+00, -5.95008469e+00, -4.99026012e+00, -1.32351279e+00, -5.01590669e-01,  2.95195317e+00,  4.62497950e+00,  5.44099808e+00,  1.17575951e+01,  1.19310246e+01,  1.50337820e+01,  1.79441051e+01,  1.80985184e+01],
-             [-2.29424934e+01, -1.80575047e+01, -1.62370167e+01, -1.56745434e+01, -8.23033428e+00, -7.16741085e+00, -6.62496185e+00, -5.73856449e+00, -1.48688376e+00,  1.80971527e+00,  2.45554900e+00,  3.85232139e+00,  4.23087120e+00,  5.92445564e+00,  6.44421244e+00,  8.21325207e+00,  1.44543571e+01,  1.44987440e+01],
-             [-2.29404392e+01, -1.83383312e+01, -1.57138681e+01, -1.54623451e+01, -1.01436739e+01, -9.37874889e+00, -4.06893778e+00, -3.25271797e+00, -1.23538244e+00, -4.17988628e-01,  1.19791162e+00,  2.69611549e+00,  3.94141436e+00,  6.43033361e+00,  8.37113857e+00,  9.67157173e+00,  1.41308174e+01,  1.42368813e+01],
-             [-2.29336300e+01, -1.85238571e+01, -1.51972685e+01, -1.51972666e+01, -1.13513584e+01, -1.05228834e+01, -2.21334386e+00, -2.21334243e+00, -3.03742558e-01, -3.03741843e-01, -9.65526607e-03,  8.24528575e-01,  1.84810734e+00,  7.89270067e+00,  1.01749058e+01,  1.01749077e+01,  1.34912348e+01,  1.40874834e+01]])
-
+        expected_bands =np.array([[-2.48738842e+01, -1.29387579e+01, -1.29387531e+01,
+        -1.29387484e+01, -1.10866852e+01, -8.07882595e+00,
+        -8.07881832e+00, -8.07881737e+00,  9.56978989e+00,
+         9.56979179e+00,  1.25314865e+01,  1.25314980e+01,
+         1.25315008e+01,  4.23717499e+01,  4.23717537e+01,
+         4.32215462e+01,  4.32215538e+01,  4.32215614e+01],
+       [-2.41191730e+01, -1.61150684e+01, -1.42791767e+01,
+        -1.42791719e+01, -1.03606348e+01, -8.68642616e+00,
+        -5.90601444e+00, -5.90600967e+00,  2.25788760e+00,
+         5.51916361e+00,  5.51916647e+00,  5.61485243e+00,
+         5.91050625e+00,  2.50469570e+01,  2.82641335e+01,
+         2.82641392e+01,  2.84246483e+01,  3.07487679e+01],
+       [-2.29339600e+01, -1.85236092e+01, -1.51975479e+01,
+        -1.51975431e+01, -1.13508320e+01, -1.05211630e+01,
+        -2.21044850e+00, -2.21044683e+00, -3.01877409e-01,
+        -3.01874220e-01, -5.50282327e-03,  8.32842171e-01,
+         1.85165548e+00,  7.89621782e+00,  1.01784697e+01,
+         1.01784744e+01,  1.34970484e+01,  1.40907288e+01],
+       [-2.29477081e+01, -1.84168339e+01, -1.56979408e+01,
+        -1.50834084e+01, -1.10056238e+01, -9.68981457e+00,
+        -2.91448879e+00, -2.63903880e+00, -1.43107760e+00,
+        -4.35178548e-01,  1.01621652e+00,  1.08422828e+00,
+         3.61865139e+00,  7.17336273e+00,  9.30155849e+00,
+         1.00361338e+01,  1.38237772e+01,  1.42762747e+01],
+       [-2.30109749e+01, -1.81430511e+01, -1.63737125e+01,
+        -1.47896776e+01, -1.06530704e+01, -7.59097767e+00,
+        -4.40895557e+00, -4.01798630e+00, -1.59009695e+00,
+        -8.00317004e-02,  1.07963777e+00,  2.36884308e+00,
+         6.40078640e+00,  6.44294930e+00,  8.05769444e+00,
+         1.10569324e+01,  1.42315102e+01,  1.53139381e+01],
+       [-2.30109730e+01, -1.81430492e+01, -1.63737125e+01,
+        -1.47896767e+01, -1.06530619e+01, -7.59097576e+00,
+        -4.40895939e+00, -4.01798677e+00, -1.59010005e+00,
+        -8.00331011e-02,  1.07963753e+00,  2.36884212e+00,
+         6.40078783e+00,  6.44294262e+00,  8.05769157e+00,
+         1.10569296e+01,  1.42315063e+01,  1.53139343e+01],
+       [-2.40614185e+01, -1.67648468e+01, -1.50328369e+01,
+        -1.34554472e+01, -9.01719761e+00, -7.44597864e+00,
+        -7.16829062e+00, -6.34035254e+00,  2.99600887e+00,
+         3.46569014e+00,  4.46293497e+00,  5.20906973e+00,
+         7.81929064e+00,  2.53426857e+01,  2.59447289e+01,
+         2.75785408e+01,  2.84667950e+01,  2.92152786e+01],
+       [-2.48738842e+01, -1.29387579e+01, -1.29387531e+01,
+        -1.29387484e+01, -1.10866852e+01, -8.07882595e+00,
+        -8.07881832e+00, -8.07881737e+00,  9.56978989e+00,
+         9.56979179e+00,  1.25314865e+01,  1.25314980e+01,
+         1.25315008e+01,  4.23717499e+01,  4.23717537e+01,
+         4.32215462e+01,  4.32215538e+01,  4.32215614e+01],
+       [-2.43795700e+01, -1.64551907e+01, -1.34434357e+01,
+        -1.34434280e+01, -1.03514872e+01, -7.39513445e+00,
+        -7.39513063e+00, -6.10492849e+00,  4.66958141e+00,
+         4.66959238e+00,  6.74782705e+00,  6.74782896e+00,
+         9.39744949e+00,  3.10566292e+01,  3.10566330e+01,
+         3.17376080e+01,  3.17376156e+01,  3.35952110e+01],
+       [-2.35389977e+01, -1.85056343e+01, -1.37990303e+01,
+        -1.37990227e+01, -1.08380346e+01, -7.56264687e+00,
+        -7.56264400e+00, -3.32416415e+00, -5.02199292e-01,
+        -5.02196670e-01,  4.68519068e+00,  4.68519211e+00,
+         7.63819790e+00,  1.68208370e+01,  1.68208504e+01,
+         2.04270325e+01,  2.04270401e+01,  2.11967201e+01],
+       [-2.31957893e+01, -1.82630310e+01, -1.56345882e+01,
+        -1.44928350e+01, -9.23434162e+00, -7.92852402e+00,
+        -5.95022917e+00, -4.99064732e+00, -1.32462871e+00,
+        -5.02915382e-01,  2.95047784e+00,  4.62426805e+00,
+         5.43929529e+00,  1.17566347e+01,  1.19293985e+01,
+         1.50316639e+01,  1.79418583e+01,  1.80968723e+01],
+       [-2.29426270e+01, -1.80574532e+01, -1.62369022e+01,
+        -1.56748543e+01, -8.22931576e+00, -7.16701651e+00,
+        -6.62407303e+00, -5.73891783e+00, -1.48577607e+00,
+         1.81063569e+00,  2.45844960e+00,  3.85335517e+00,
+         4.23286629e+00,  5.92695141e+00,  6.44531107e+00,
+         8.21370125e+00,  1.44566507e+01,  1.44983978e+01],
+       [-2.29406662e+01, -1.83379650e+01, -1.57140999e+01,
+        -1.54625340e+01, -1.01433325e+01, -9.37792110e+00,
+        -4.06694746e+00, -3.25191188e+00, -1.23258555e+00,
+        -4.15170372e-01,  1.20202124e+00,  2.69886231e+00,
+         3.94393396e+00,  6.43281651e+00,  8.37365723e+00,
+         9.67361927e+00,  1.41347656e+01,  1.42384768e+01],
+       [-2.29339600e+01, -1.85236092e+01, -1.51975479e+01,
+        -1.51975431e+01, -1.13508320e+01, -1.05211630e+01,
+        -2.21044850e+00, -2.21044683e+00, -3.01877409e-01,
+        -3.01874220e-01, -5.50282327e-03,  8.32842171e-01,
+         1.85165548e+00,  7.89621782e+00,  1.01784697e+01,
+         1.01784744e+01,  1.34970484e+01,  1.40907288e+01]], dtype=np.float32)
         assert np.allclose(eigenstatus["eigenvalues"], expected_bands, atol=1e-4)
 
 
@@ -99,25 +166,79 @@ def test_bands(self):
             device=model.device)
 
         stru_data = f"{rootdir}/json_model/silicon.vasp"
-        AtomicData_options = {"r_max": 2.6, "oer_max":2.5, "pbc": True}
+        AtomicData_options = {"r_max": 2.6, "oer_max":2.5}
 
         eigenstatus = bcal.get_bands(data=stru_data, 
-               kpath_kwargs=kpath_kwargs, 
-               AtomicData_options=AtomicData_options)
+               kpath_kwargs=kpath_kwargs)
         
-        expected_bands =np.array([[-20.259584  ,  -8.328452  ,  -8.328452  ,  -8.328451  ,  -5.782879  ,  -5.782879  ,  -5.7828774 ,  -4.800206  ,  -0.8470682 ,  -0.8470663 ,   4.9619126 ,   4.961913  ,   4.9619136 ,   6.4527135 ,   6.452714  ,   6.452715  ,  10.1427765 ,  10.142781  ],
-                                  [-19.173727  , -11.876228  , -10.340221  , -10.34022   ,  -6.861969  ,  -4.9920564 ,  -2.1901789 ,  -2.1901765 ,  -0.9258757 ,   0.76235735,   4.2745295 ,   4.2745323 ,   4.990632  ,   5.55916   ,   5.559161  ,   8.533346  ,   8.716906  ,  11.661528  ],
-                                  [-16.172304  , -16.172298  , -11.271987  , -11.271983  ,  -7.4252186 ,  -7.4252176 ,   2.1354833 ,   2.135485  ,   2.4157436 ,   2.4157462 ,   2.7901921 ,   2.7901928 ,   3.6496053 ,   3.649607  ,   4.6478515 ,   4.6478524 ,  11.951376  ,  11.951382  ],
-                                  [-16.322428  , -15.988458  , -11.912281  , -11.193047  ,  -7.3037252 ,  -6.193884  ,   1.205529  ,   1.386399  ,   1.6548665 ,   1.8747401 ,   2.580269  ,   3.005812  ,   3.4153423 ,   4.022218  ,   5.4699235 ,   6.23605   ,  11.671546  ,  11.832637  ],
-                                  [-16.799667  , -15.46194   , -12.612725  , -10.942198  ,  -6.9641047 ,  -3.7625234 ,  -0.7360446 ,   0.28918347,   0.47772366,   0.6291326 ,   2.4882295 ,   3.1617444 ,   4.0417986 ,   4.6302714 ,   6.5749364 ,   8.062847  ,  10.855666  ,  11.509191  ],
-                                  [-16.799667  , -15.461945  , -12.612727  , -10.9422035 ,  -6.9641085 ,  -3.7625222 ,  -0.73604566,   0.28918162,   0.4777242 ,   0.62913096,   2.4882276 ,   3.1617427 ,   4.041798  ,   4.6302724 ,   6.5749335 ,   8.062847  ,  10.855668  ,  11.509187  ],
-                                  [-19.12568   , -12.3842125 , -11.161121  ,  -9.196095  ,  -5.6751695 ,  -4.8814125 ,  -3.031833  ,  -2.0943422 ,  -2.0460339 ,   0.7482071 ,   3.5014281 ,   4.8715053 ,   5.2672033 ,   5.640518  ,   6.8847284 ,   7.1940207 ,  10.2244625 ,  10.705325  ],
-                                  [-20.259584  ,  -8.328452  ,  -8.328452  ,  -8.328451  ,  -5.782879  ,  -5.782879  ,  -5.7828774 ,  -4.800206  ,  -0.8470682 ,  -0.8470663 ,   4.9619126 ,   4.961913  ,   4.9619136 ,   6.4527135 ,   6.452714  ,   6.452715  ,  10.1427765 ,  10.142781  ],
-                                  [-19.503462  , -12.068741  ,  -9.1723    ,  -9.172297  ,  -6.1124167 ,  -4.959279  ,  -4.959278  ,  -1.1632957 ,  -1.1632944 ,  -1.1617142 ,   4.8985996 ,   5.257441  ,   5.257443  ,   6.191231  ,   6.2036867 ,   6.203688  ,  10.432747  ,  10.432751  ],
-                                  [-18.410772  , -14.457038  ,  -9.623036  ,  -9.623032  ,  -6.8522253 ,  -5.3134403 ,  -5.3134394 ,   0.34697238,   0.3469742 ,   1.5420008 ,   3.4220562 ,   3.4220574 ,   5.17151   ,   5.250026  ,   7.019237  ,   7.0192394 ,  10.747205  ,  10.747212  ],
-                                  [-17.752392  , -14.654745  , -11.930272  , -10.688241  ,  -5.6049733 ,  -4.517258  ,  -2.4019077 ,  -0.54922515,  -0.42735893,   1.6003915 ,   2.3744426 ,   3.288959  ,   4.6278877 ,   4.90705   ,   7.08742   ,   9.220286  ,   9.723419  ,  11.138031  ],
-                                  [-16.101318  , -16.101318  , -12.243194  , -12.243191  ,  -3.945867  ,  -3.9458647 ,  -2.42533   ,  -2.4253287 ,   2.3399496 ,   2.3399508 ,   2.8937058 ,   2.893708  ,   3.2351081 ,   3.235109  ,   7.9230847 ,   7.9230857 ,  11.04461   ,  11.044615  ],
-                                  [-16.138231  , -16.138226  , -11.826924  , -11.826924  ,  -6.087353  ,  -6.087353  ,   0.08484415,   0.08484493,   2.342462  ,   2.3424625 ,   2.8806267 ,   2.8806279 ,   3.2753062 ,   3.2753084 ,   6.610969  ,   6.6109715 ,  11.579055  ,  11.579056  ],
-                                  [-16.172304  , -16.172298  , -11.271987  , -11.271983  ,  -7.4252186 ,  -7.4252176 ,   2.1354833 ,   2.135485  ,   2.4157436 ,   2.4157462 ,   2.7901921 ,   2.7901928 ,   3.6496053 ,   3.649607  ,   4.6478515 ,   4.6478524 ,  11.951376  ,  11.951382  ]])
-
+        expected_bands =np.array([[-28.032394  , -12.518021  ,  -8.789028  ,  -8.789027  ,
+         -8.78902   ,  -6.074078  ,  -6.074069  ,  -6.0740604 ,
+         17.192019  ,  17.192028  ,  22.030336  ,  22.030338  ,
+         22.03035   ,  23.343376  ,  23.343376  ,  23.343384  ,
+         28.18668   ,  28.186697  ],
+       [-26.710665  , -17.258825  , -11.786415  , -11.786402  ,
+         -6.316819  ,  -6.08972   ,  -2.2474113 ,  -2.2474105 ,
+         15.599638  ,  18.773561  ,  20.637032  ,  21.751331  ,
+         21.751333  ,  22.788795  ,  22.788813  ,  26.043669  ,
+         26.558607  ,  29.842487  ],
+       [-22.908417  , -22.90841   , -13.267318  , -13.267316  ,
+         -5.855864  ,  -5.8558598 ,   0.13847035,   0.13847637,
+         17.0159    ,  17.0159    ,  21.383863  ,  21.383865  ,
+         22.246996  ,  22.247007  ,  22.64281   ,  22.642822  ,
+         29.825714  ,  29.825722  ],
+       [-23.125595  , -22.677975  , -13.552594  , -13.126421  ,
+         -6.040592  ,  -5.239112  ,  -0.16200367,   0.15783598,
+         17.022974  ,  17.076164  ,  20.278925  ,  21.015097  ,
+         21.579382  ,  22.268646  ,  23.596603  ,  24.191101  ,
+         29.491728  ,  29.689163  ],
+       [-23.748362  , -21.997149  , -13.956201  , -12.712631  ,
+         -6.524768  ,  -3.9821868 ,  -0.98925126,   0.1548973 ,
+         17.227066  ,  17.242361  ,  18.778227  ,  20.15013   ,
+         22.017757  ,  22.306322  ,  24.653385  ,  25.92878   ,
+         28.555614  ,  29.325285  ],
+       [-23.748354  , -21.997162  , -13.956195  , -12.712616  ,
+         -6.5247726 ,  -3.9821932 ,  -0.98925066,   0.15489526,
+         17.22706   ,  17.242352  ,  18.778234  ,  20.150133  ,
+         22.017756  ,  22.306314  ,  24.653393  ,  25.928793  ,
+         28.555605  ,  29.325268  ],
+       [-26.629864  , -17.600563  , -12.377507  , -10.340946  ,
+         -8.252066  ,  -4.5056643 ,  -3.1107721 ,  -1.5953344 ,
+         16.209995  ,  17.483477  ,  20.519865  ,  21.010235  ,
+         22.459747  ,  22.790142  ,  24.399433  ,  24.6775    ,
+         28.152231  ,  28.817282  ],
+       [-28.032394  , -12.518021  ,  -8.789028  ,  -8.789027  ,
+         -8.78902   ,  -6.074078  ,  -6.074069  ,  -6.0740604 ,
+         17.192019  ,  17.192028  ,  22.030336  ,  22.030338  ,
+         22.03035   ,  23.343376  ,  23.343376  ,  23.343384  ,
+         28.18668   ,  28.186697  ],
+       [-27.095016  , -16.587624  , -10.282119  , -10.282109  ,
+         -9.392605  ,  -4.47474   ,  -4.4747334 ,  -2.0351746 ,
+         16.790812  ,  16.79082   ,  21.469982  ,  21.469984  ,
+         22.261318  ,  23.112501  ,  23.889814  ,  23.889832  ,
+         28.476255  ,  28.476261  ],
+       [-25.650513  , -19.811338  , -11.168917  , -11.168911  ,
+         -9.854099  ,  -3.526547  ,  -3.5265448 ,   0.52536994,
+         17.867775  ,  17.86778   ,  18.799477  ,  18.799482  ,
+         22.678572  ,  22.67859   ,  25.41526   ,  25.415262  ,
+         28.463633  ,  28.463642  ],
+       [-24.895058  , -20.650934  , -13.44941   , -12.120061  ,
+         -7.257396  ,  -3.5902717 ,  -2.117014  ,   0.15689819,
+         17.095425  ,  17.784517  ,  18.656044  ,  19.36713   ,
+         22.366032  ,  22.408947  ,  25.288326  ,  26.88247   ,
+         27.520784  ,  28.909798  ],
+       [-22.888115  , -22.888107  , -13.577753  , -13.577752  ,
+         -5.001314  ,  -5.0013084 ,  -0.38720337,  -0.38719827,
+         17.119455  ,  17.11947   ,  19.495407  ,  19.495422  ,
+         21.823872  ,  21.823875  ,  25.932095  ,  25.9321    ,
+         28.705719  ,  28.705719  ],
+       [-22.898304  , -22.898302  , -13.427348  , -13.427342  ,
+         -5.4427934 ,  -5.442793  ,  -0.10512064,  -0.10511683,
+         17.060398  ,  17.060402  ,  20.222359  ,  20.222364  ,
+         21.842312  ,  21.842323  ,  24.5921    ,  24.5921    ,
+         29.378563  ,  29.378567  ],
+       [-22.908417  , -22.90841   , -13.267318  , -13.267316  ,
+         -5.855864  ,  -5.8558598 ,   0.13847035,   0.13847637,
+         17.0159    ,  17.0159    ,  21.383863  ,  21.383865  ,
+         22.246996  ,  22.247007  ,  22.64281   ,  22.642822  ,
+         29.825714  ,  29.825722  ]], dtype=np.float32)
         assert np.allclose(eigenstatus["eigenvalues"], expected_bands, atol=1e-4)
\ No newline at end of file
diff --git a/dptb/tests/test_get_fermi.py b/dptb/tests/test_get_fermi.py
index cc0df8c0..51d55fd1 100644
--- a/dptb/tests/test_get_fermi.py
+++ b/dptb/tests/test_get_fermi.py
@@ -9,23 +9,16 @@
 
 
 def test_get_fermi():
-    ckpt = f"{rootdir}/test_get_fermi/nnsk.best.pth"
+    ckpt = f"{rootdir}/test_get_fermi/nnsk.best.pth"  #  'hopping': {'method': 'poly2exp', 'rs': 5.0, 'w': 0.6},
     stru_data = f"{rootdir}/test_get_fermi/PRIMCELL.vasp"
 
     model = build_model(checkpoint=ckpt)
-    AtomicData_options={
-            "r_max": 5.50,
-            "pbc": True
-        }
-
-    AtomicData_options = AtomicData_options
     nel_atom = {"Au":11}
 
     elec_cal = ElecStruCal(model=model,device='cpu')
     _, efermi =elec_cal.get_fermi_level(data=stru_data, 
                     nel_atom = nel_atom,
-                meshgrid=[30,30,30],
-                AtomicData_options=AtomicData_options)
+                meshgrid=[30,30,30])
     
-    assert abs(efermi  + 3.25725233554) < 1e-5
+    assert abs(efermi  + 3.194006085395813) < 1e-5
 
diff --git a/dptb/tests/test_multi_batch.py b/dptb/tests/test_multi_batch.py
index 00bedb93..5f468b38 100644
--- a/dptb/tests/test_multi_batch.py
+++ b/dptb/tests/test_multi_batch.py
@@ -15,6 +15,9 @@
 
 class TestMultiBatch:
     set_options = {
+    "r_max": 5.0,
+    "er_max": 5.0,
+    "oer_max": 2.5,
     "root": f"{rootdir}/test_sktb/dataset",
     "prefix": "kpathmd25",
     "get_eigenvalues": True,
diff --git a/dptb/tests/test_nnsk.py b/dptb/tests/test_nnsk.py
index 11f79517..ca5ded30 100644
--- a/dptb/tests/test_nnsk.py
+++ b/dptb/tests/test_nnsk.py
@@ -39,6 +39,9 @@ class TestNNSK:
         "push": None}
     }
     data_options = {
+        "r_max": 2.6,
+        "er_max": 2.6,
+        "oer_max":1.6,
         "train": {
             "root": f"{rootdir}/hBN/dataset",
             "prefix": "kpath",
@@ -46,7 +49,7 @@ class TestNNSK:
         }
     }
 
-    train_datasets = build_dataset(**data_options["train"], **common_options)
+    train_datasets = build_dataset(**data_options, **data_options["train"], **common_options)
     train_loader = DataLoader(dataset=train_datasets, batch_size=1, shuffle=True)
 
     batch = next(iter(train_loader))
diff --git a/dptb/tests/test_nrl.py b/dptb/tests/test_nrl.py
index def169ea..2c0a1d63 100644
--- a/dptb/tests/test_nrl.py
+++ b/dptb/tests/test_nrl.py
@@ -44,7 +44,7 @@ def test_nrl_json_band():
     }
 
     stru_data = f"{rootdir}/json_model/silicon.vasp"
-    AtomicData_options = {"r_max": 5.0, "oer_max":6.6147151362875, "pbc": True}
+    AtomicData_options = {"r_max": 5.0, "oer_max":6.6147151362875}
     kpath_kwargs = jdata["task_options"]
     bcal = Band(model=model, 
                 use_gui=True, 
@@ -52,24 +52,64 @@ def test_nrl_json_band():
                 device=model.device)
 
     eigenstatus = bcal.get_bands(data=stru_data, 
-                   kpath_kwargs=kpath_kwargs, 
-                   AtomicData_options=AtomicData_options)
-    
-    expected_eigenvalues = np.array([[-6.1745434 ,  5.282297  ,  5.282303  ,  5.2823052 ,  8.658317  ,  8.6583185 ,  8.658324  ,  9.862869  , 14.152446  , 14.152451  , 15.180438  , 15.180452  , 16.983887  , 16.983889  , 16.983896  , 23.09491   , 23.094921  , 23.094925  ],
-                                     [-5.5601606 ,  2.1920488 ,  3.4229636 ,  3.4229672 ,  7.347074  ,  9.382092  , 11.1772175 , 11.177221  , 14.349099  , 14.924912  , 15.062427  , 15.064081  , 16.540335  , 16.54034   , 20.871534  , 20.871536  , 21.472364  , 28.740482  ],
-                                     [-2.556269  , -2.5562677 ,  2.3915231 ,  2.391524  ,  6.4689007 ,  6.468908  , 14.639398  , 14.6394005 , 14.734453  , 14.734456  , 14.747707  , 14.74771   , 15.57567   , 15.575676  , 17.403324  , 17.403334  , 38.39217   , 38.392174  ],
-                                     [-2.6333795 , -2.367625  ,  1.6872846 ,  2.5042236 ,  6.6183453 ,  7.9818068 , 13.933364  , 14.267717  , 14.706404  , 14.793142  , 14.841357  , 15.211192  , 15.578381  , 15.838447  , 17.168877  , 18.059359  , 35.321945  , 37.87687   ],
-                                     [-2.9967206 , -1.8161079 ,  0.88636655,  2.829976  ,  7.0469265 , 10.600885  , 12.648353  , 13.126463  , 14.653016  , 14.841116  , 15.541919  , 15.576077  , 16.276308  , 16.574654  , 17.213411  , 19.315798  , 28.62305   , 35.468586  ],
-                                     [-2.996724  , -1.8161156 ,  0.88636786,  2.8299737 ,  7.046927  , 10.600888  , 12.648361  , 13.126465  , 14.653028  , 14.841116  , 15.541907  , 15.5760765 , 16.276312  , 16.574644  , 17.21341   , 19.315798  , 28.623045  , 35.46858   ],
-                                     [-5.471941  ,  1.5238439 ,  2.5368657 ,  4.577535  ,  8.749301  ,  9.402245  , 10.557684  , 11.247256  , 14.576941  , 14.75164   , 14.775435  , 15.122616  , 17.103615  , 17.840292  , 18.390976  , 22.68788   , 23.806395  , 24.265633  ],
-                                     [-6.1745434 ,  5.282297  ,  5.282303  ,  5.2823052 ,  8.658317  ,  8.6583185 ,  8.658324  ,  9.862869  , 14.152446  , 14.152451  , 15.180438  , 15.180452  , 16.983887  , 16.983889  , 16.983896  , 23.09491   , 23.094921  , 23.094925  ],
-                                     [-5.749872  ,  1.7248219 ,  4.5455103 ,  4.545513  ,  8.227031  ,  9.438793  ,  9.4388    , 11.6675415 , 14.485937  , 14.485939  , 14.894153  , 14.894157  , 16.697474  , 16.697474  , 19.904425  , 23.02558   , 23.025585  , 23.831646  ],
-                                     [-4.44458   , -1.6045983 ,  4.0464916 ,  4.046497  ,  7.2234683 ,  9.777258  ,  9.777259  , 14.115966  , 14.4775715 , 14.4775715 , 14.98191   , 14.9819145 , 16.346727  , 16.346727  , 18.716038  , 23.819721  , 23.819735  , 27.016748  ],
-                                     [-3.8950639 , -1.3644799 ,  1.8130541 ,  3.112887  ,  8.6044655 ,  9.8463125 , 11.3755455 , 12.709737  , 14.566758  , 14.910749  , 15.183235  , 15.717886  , 16.694214  , 17.240337  , 19.386671  , 21.171314  , 23.601032  , 29.806623  ],
-                                     [-2.3356187 , -2.3356178 ,  1.3771206 ,  1.3771234 , 10.240082  , 10.240085  , 12.212795  , 12.212798  , 14.746381  , 14.746386  , 15.778043  , 15.778048  , 15.790003  , 15.790005  , 18.402258  , 18.40226   , 31.99752   , 31.997526  ],
-                                     [-2.4508858 , -2.4508843 ,  1.809629  ,  1.809632  ,  8.082377  ,  8.082378  , 13.7137    , 13.713703  , 14.742302  , 14.742307  , 15.081548  , 15.081549  , 15.864478  , 15.864485  , 17.778458  , 17.77847   , 35.317     , 35.317005  ],
-                                     [-2.556269  , -2.5562677 ,  2.3915231 ,  2.391524  ,  6.4689007 ,  6.468908  , 14.639398  , 14.6394005 , 14.734453  , 14.734456  , 14.747707  , 14.74771   , 15.57567   , 15.575676  , 17.403324  , 17.403334  , 38.39217   , 38.392174  ]])
+                   kpath_kwargs=kpath_kwargs)
     
+    expected_eigenvalues = np.array([[-6.1741133 ,  5.2992673 ,  5.299269  ,  5.2992706 ,  8.679379  ,
+         8.67938   ,  8.679387  ,  9.836669  , 14.15181   , 14.151812  ,
+        15.179906  , 15.179909  , 17.065308  , 17.065311  , 17.065315  ,
+        23.384512  , 23.384514  , 23.384523  ],
+       [-5.5645704 ,  2.1704118 ,  3.4521012 ,  3.4521055 ,  7.330651  ,
+         9.427716  , 11.252065  , 11.252069  , 14.348874  , 14.904958  ,
+        15.063788  , 15.08024   , 16.522131  , 16.522133  , 20.978777  ,
+        20.97878   , 21.235731  , 28.363321  ],
+       [-2.554551  , -2.5545506 ,  2.4126623 ,  2.4126637 ,  6.4693484 ,
+         6.46935   , 14.620965  , 14.620967  , 14.736008  , 14.736009  ,
+        14.747112  , 14.747118  , 15.574924  , 15.574924  , 17.599064  ,
+        17.599068  , 38.834724  , 38.83473   ],
+       [-2.6305206 , -2.3678906 ,  1.7033997 ,  2.5220068 ,  6.6189265 ,
+         7.990758  , 13.693519  , 14.290318  , 14.706135  , 14.793065  ,
+        14.83984   , 15.134137  , 15.58144   , 15.826494  , 17.384142  ,
+        18.580969  , 35.741035  , 37.842724  ],
+       [-2.990186  , -1.8204781 ,  0.89282584,  2.8375692 ,  7.0482116 ,
+        10.63623   , 12.305137  , 13.244209  , 14.652864  , 14.841782  ,
+        15.511359  , 15.560423  , 16.049604  , 16.561003  , 17.382034  ,
+        20.065767  , 28.94485   , 34.8759    ],
+       [-2.9901865 , -1.8204774 ,  0.8928198 ,  2.8375793 ,  7.0482135 ,
+        10.636231  , 12.305133  , 13.244207  , 14.65287   , 14.8417845 ,
+        15.51136   , 15.560425  , 16.049616  , 16.560993  , 17.38204   ,
+        20.065779  , 28.944853  , 34.8759    ],
+       [-5.47864   ,  1.5369629 ,  2.5553446 ,  4.5224996 ,  8.77259   ,
+         9.497431  , 10.579291  , 11.207781  , 14.566749  , 14.716702  ,
+        14.79304   , 15.098012  , 17.310905  , 17.856058  , 18.338556  ,
+        22.161259  , 23.587708  , 24.457659  ],
+       [-6.1741133 ,  5.2992673 ,  5.299269  ,  5.2992706 ,  8.679379  ,
+         8.67938   ,  8.679387  ,  9.836669  , 14.15181   , 14.151812  ,
+        15.179906  , 15.179909  , 17.065308  , 17.065311  , 17.065315  ,
+        23.384512  , 23.384514  , 23.384523  ],
+       [-5.7577815 ,  1.7521204 ,  4.532131  ,  4.532138  ,  8.251087  ,
+         9.490989  ,  9.490992  , 11.636496  , 14.439946  , 14.439954  ,
+        14.857351  , 14.857357  , 16.893194  , 16.8932    , 19.772648  ,
+        22.80723   , 22.807241  , 23.869514  ],
+       [-4.443464  , -1.6066544 ,  4.0123796 ,  4.012383  ,  7.202861  ,
+         9.727551  ,  9.72756   , 13.991438  , 14.478059  , 14.478066  ,
+        14.982593  , 14.982594  , 16.354605  , 16.354612  , 18.78879   ,
+        23.787397  , 23.7874    , 27.123451  ],
+       [-3.8896487 , -1.3633558 ,  1.791838  ,  3.1084414 ,  8.638544  ,
+         9.726504  , 11.305393  , 12.739989  , 14.571893  , 14.913423  ,
+        15.175632  , 15.675038  , 16.678635  , 17.124842  , 19.235235  ,
+        21.592587  , 24.378092  , 28.834328  ],
+       [-2.3344338 , -2.334433  ,  1.3823981 ,  1.3824005 , 10.204421  ,
+        10.204421  , 12.187885  , 12.187888  , 14.745355  , 14.745364  ,
+        15.736717  , 15.736728  , 15.787358  , 15.787364  , 18.632502  ,
+        18.632504  , 31.974377  , 31.974384  ],
+       [-2.449809  , -2.4498005 ,  1.8228805 ,  1.8228889 ,  8.088991  ,
+         8.088991  , 13.572681  , 13.572683  , 14.7421055 , 14.742113  ,
+        15.033702  , 15.03371   , 15.825112  , 15.825113  , 18.229067  ,
+        18.229078  , 35.388714  , 35.388718  ],
+       [-2.554551  , -2.5545506 ,  2.4126623 ,  2.4126637 ,  6.4693484 ,
+         6.46935   , 14.620965  , 14.620967  , 14.736008  , 14.736009  ,
+        14.747112  , 14.747118  , 15.574924  , 15.574924  , 17.599064  ,
+        17.599068  , 38.834724  , 38.83473   ]], dtype=np.float32)
 
     assert np.allclose(eigenstatus["eigenvalues"], expected_eigenvalues, atol=1e-4)
 
diff --git a/dptb/tests/test_soc.py b/dptb/tests/test_soc.py
index 9ca18220..c50d3974 100644
--- a/dptb/tests/test_soc.py
+++ b/dptb/tests/test_soc.py
@@ -43,148 +43,147 @@ def test_soc_json_band():
                 results_path='./', 
                 device=model.device)
     
-    stru_data = f"{rootdir}/Sn/soc/dataset/Sn.vasp"
-    AtomicData_options = {"r_max": 6.0, "oer_max":3.0, "pbc": True}
-    
+    stru_data = f"{rootdir}/Sn/soc/dataset/Sn.vasp"    
     eigenstatus = bcal.get_bands(data=stru_data, 
-                   kpath_kwargs=kpath_kwargs, 
-                   AtomicData_options=AtomicData_options)
+                   kpath_kwargs=kpath_kwargs)
 
-    expected_eigenvalues = np.array([[-18.796585  , -18.796577  ,  -8.796718  ,  -8.796717  ,
-         -8.467822  ,  -8.46782   ,  -8.202273  ,  -8.202273  ,
-         -8.202272  ,  -8.202268  ,  -6.520131  ,  -6.5201283 ,
-         -5.6209826 ,  -5.6209826 ,  -5.6209803 ,  -5.620978  ,
-          1.1558133 ,   1.1558162 ,   1.1558177 ,   1.1558225 ,
-          3.8549075 ,   3.8549078 ,   3.8549087 ,   3.8549109 ,
-         10.136021  ,  10.136023  ,  10.439617  ,  10.439617  ,
-         10.439621  ,  10.439622  ,  16.604067  ,  16.60407   ,
-         16.604074  ,  16.604078  ,  16.60518   ,  16.60518   ],
-       [-17.965763  , -17.965757  , -12.480173  , -12.480173  ,
-        -10.082873  , -10.082871  , -10.001692  , -10.001686  ,
-         -6.423237  ,  -6.423237  ,  -5.713276  ,  -5.713276  ,
-         -3.4990137 ,  -3.4990127 ,  -3.2606316 ,  -3.260627  ,
-         -1.2786437 ,  -1.278643  ,  -0.16114095,  -0.16113918,
-          2.7485087 ,   2.7485092 ,   2.9157577 ,   2.9157612 ,
-          9.215347  ,   9.215352  ,   9.373201  ,   9.373207  ,
-         13.123983  ,  13.123989  ,  14.268081  ,  14.268081  ,
-         14.278081  ,  14.278091  ,  15.152899  ,  15.1529045 ],
-       [-15.753845  , -15.753845  , -15.753841  , -15.753836  ,
-        -11.016632  , -11.016631  , -11.016631  , -11.01663   ,
-         -6.9324965 ,  -6.932495  ,  -6.932495  ,  -6.9324923 ,
-         -0.52784276,  -0.52784216,  -0.52783954,  -0.5278391 ,
-          0.07181728,   0.07181839,   0.07181882,   0.07181931,
-          0.08202878,   0.08202914,   0.08203014,   0.08203081,
-          6.5781436 ,   6.5781474 ,   6.5781474 ,   6.578152  ,
-         12.591245  ,  12.591249  ,  12.591251  ,  12.591253  ,
-         14.805855  ,  14.805855  ,  14.805863  ,  14.805869  ],
-       [-15.858617  , -15.858613  , -15.649114  , -15.64911   ,
-        -11.349122  , -11.34912   , -10.8918085 , -10.891806  ,
-         -6.870063  ,  -6.8700614 ,  -6.157268  ,  -6.1572657 ,
-         -1.2535331 ,  -1.253531  ,  -1.0800866 ,  -1.0800853 ,
-         -0.28412414,  -0.28412127,  -0.22913142,  -0.2291308 ,
-          0.43651295,   0.43651417,   0.61508846,   0.6150922 ,
-          6.995065  ,   6.995071  ,   7.036594  ,   7.0365944 ,
-         11.637238  ,  11.637241  ,  12.705406  ,  12.705407  ,
-         14.5028715 ,  14.502877  ,  15.520835  ,  15.520839  ],
-       [-16.183804  , -16.183802  , -15.329778  , -15.329772  ,
-        -11.730029  , -11.730025  , -10.597874  , -10.597873  ,
-         -6.6675787 ,  -6.667575  ,  -4.5243273 ,  -4.524324  ,
-         -2.293503  ,  -2.2935014 ,  -1.9981623 ,  -1.9981592 ,
-         -1.0307596 ,  -1.0307531 ,  -0.8473131 ,  -0.847308  ,
-          0.8623935 ,   0.86239386,   1.1516515 ,   1.1516529 ,
-          7.940049  ,   7.9400563 ,   8.233609  ,   8.23361   ,
-         10.271218  ,  10.27122   ,  13.005727  ,  13.00573   ,
-         13.675878  ,  13.675881  ,  16.305794  ,  16.3058    ],
-       [-16.336843  , -16.336843  , -15.167489  , -15.167487  ,
-        -11.641985  , -11.641981  , -10.840412  , -10.840409  ,
-         -5.5028615 ,  -5.502861  ,  -4.4378824 ,  -4.437881  ,
-         -3.1876206 ,  -3.1876204 ,  -2.8593407 ,  -2.8593404 ,
-         -1.0223196 ,  -1.0223194 ,  -0.03593016,  -0.0359299 ,
-          0.5331634 ,   0.53316516,   1.094498  ,   1.0945    ,
-          8.331565  ,   8.331567  ,   9.181907  ,   9.181908  ,
-         10.318527  ,  10.31853   ,  11.164237  ,  11.16424   ,
-         14.783967  ,  14.783967  ,  16.183851  ,  16.18386   ],
-       [-16.183802  , -16.183798  , -15.329779  , -15.329777  ,
-        -11.730029  , -11.730021  , -10.597873  , -10.59787   ,
-         -6.6675773 ,  -6.6675754 ,  -4.5243297 ,  -4.524327  ,
-         -2.2935047 ,  -2.2935045 ,  -1.9981617 ,  -1.9981614 ,
-         -1.0307611 ,  -1.0307586 ,  -0.84731257,  -0.8473123 ,
-          0.86239225,   0.862393  ,   1.1516463 ,   1.1516486 ,
-          7.9400487 ,   7.9400496 ,   8.23361   ,   8.233611  ,
-         10.27122   ,  10.271224  ,  13.005723  ,  13.005732  ,
-         13.675874  ,  13.675881  ,  16.305796  ,  16.305796  ],
-       [-17.900473  , -17.900473  , -12.844977  , -12.844976  ,
-        -10.530397  , -10.530396  ,  -9.09718   ,  -9.097178  ,
-         -6.7394004 ,  -6.739399  ,  -4.928301  ,  -4.9282985 ,
-         -4.1416554 ,  -4.1416535 ,  -3.2446477 ,  -3.2446465 ,
-         -1.2467662 ,  -1.2467624 ,  -0.16752447,  -0.1675212 ,
-          2.0890796 ,   2.0890806 ,   2.8771484 ,   2.87715   ,
-         10.350287  ,  10.350292  ,  10.554118  ,  10.554125  ,
-         10.833848  ,  10.833856  ,  13.666271  ,  13.666271  ,
-         14.696394  ,  14.696395  ,  15.524468  ,  15.524472  ],
-       [-18.796585  , -18.796577  ,  -8.796718  ,  -8.796717  ,
-         -8.467822  ,  -8.46782   ,  -8.202273  ,  -8.202273  ,
-         -8.202272  ,  -8.202268  ,  -6.520131  ,  -6.5201283 ,
-         -5.6209826 ,  -5.6209826 ,  -5.6209803 ,  -5.620978  ,
-          1.1558133 ,   1.1558162 ,   1.1558177 ,   1.1558225 ,
-          3.8549075 ,   3.8549078 ,   3.8549087 ,   3.8549109 ,
-         10.136021  ,  10.136023  ,  10.439617  ,  10.439617  ,
-         10.439621  ,  10.439622  ,  16.604067  ,  16.60407   ,
-         16.604074  ,  16.604078  ,  16.60518   ,  16.60518   ],
-       [-18.186668  , -18.186663  , -12.287521  , -12.287515  ,
-         -9.351741  ,  -9.351739  ,  -8.878574  ,  -8.878569  ,
-         -7.6058683 ,  -7.6058674 ,  -5.154534  ,  -5.154529  ,
-         -4.6624527 ,  -4.662451  ,  -3.4278681 ,  -3.427864  ,
-         -0.41998848,  -0.41998297,  -0.4095187 ,  -0.40951777,
-          2.9563828 ,   2.9563835 ,   2.9918096 ,   2.9918125 ,
-         10.380918  ,  10.380918  ,  10.549788  ,  10.549789  ,
-         11.232219  ,  11.232219  ,  14.940169  ,  14.940175  ,
-         15.070571  ,  15.070571  ,  15.075499  ,  15.0755005 ],
-       [-17.198385  , -17.19838   , -14.515316  , -14.515307  ,
-         -9.862983  ,  -9.862981  ,  -9.357086  ,  -9.357084  ,
-         -8.073727  ,  -8.073721  ,  -4.8036227 ,  -4.8036175 ,
-         -4.4337177 ,  -4.4337125 ,  -1.1210939 ,  -1.1210878 ,
-          0.15520462,   0.15520588,   0.16363804,   0.16363822,
-          0.24147274,   0.24147661,   0.41342714,   0.41343114,
-         10.37974   ,  10.379746  ,  10.456181  ,  10.456185  ,
-         10.552433  ,  10.552438  ,  13.742164  ,  13.742164  ,
-         13.763256  ,  13.763256  ,  14.501388  ,  14.501396  ],
-       [-16.80349   , -16.803486  , -14.751759  , -14.751757  ,
-        -11.285735  , -11.285734  , -10.22461   , -10.224609  ,
-         -6.24789   ,  -6.24789   ,  -4.6060047 ,  -4.6060038 ,
-         -3.434149  ,  -3.4341471 ,  -2.2420568 ,  -2.2420552 ,
-         -1.0955485 ,  -1.0955476 ,  -0.19254336,  -0.19254316,
-          0.6856604 ,   0.68566144,   1.1567098 ,   1.1567105 ,
-          9.163708  ,   9.16371   ,   9.635809  ,   9.635814  ,
-         10.698371  ,  10.698381  ,  11.82481   ,  11.82481   ,
-         13.985387  ,  13.985389  ,  16.133114  ,  16.133121  ],
-       [-15.776018  , -15.776018  , -15.7288265 , -15.728823  ,
-        -11.566163  , -11.566163  , -11.256174  , -11.256172  ,
-         -4.9769745 ,  -4.9769716 ,  -4.5070167 ,  -4.5070157 ,
-         -3.389411  ,  -3.3894083 ,  -3.2984304 ,  -3.2984302 ,
-          0.10734507,   0.10734744,   0.17103618,   0.17103752,
-          0.26678348,   0.26678437,   0.40296242,   0.40296602,
-          8.415588  ,   8.415589  ,   8.428018  ,   8.428018  ,
-         10.579563  ,  10.579564  ,  10.626951  ,  10.626951  ,
-         15.609341  ,  15.6093445 ,  15.68799   ,  15.688002  ],
-       [-15.769385  , -15.769385  , -15.736303  , -15.736301  ,
-        -11.354855  , -11.354855  , -11.111078  , -11.111077  ,
-         -6.22072   ,  -6.2207155 ,  -5.98496   ,  -5.9849544 ,
-         -1.8360822 ,  -1.8360817 ,  -1.750961  ,  -1.7509596 ,
-          0.07467235,   0.07467385,   0.08895914,   0.0889603 ,
-          0.30550689,   0.30550796,   0.39796725,   0.39797014,
-          7.371509  ,   7.371511  ,   7.3771377 ,   7.377142  ,
-         11.648639  ,  11.648641  ,  11.689653  ,  11.689657  ,
-         15.269731  ,  15.269734  ,  15.337125  ,  15.3371315 ],
-       [-15.753845  , -15.753845  , -15.753841  , -15.753836  ,
-        -11.016632  , -11.016631  , -11.016631  , -11.01663   ,
-         -6.9324965 ,  -6.932495  ,  -6.932495  ,  -6.9324923 ,
-         -0.52784276,  -0.52784216,  -0.52783954,  -0.5278391 ,
-          0.07181728,   0.07181839,   0.07181882,   0.07181931,
-          0.08202878,   0.08202914,   0.08203014,   0.08203081,
-          6.5781436 ,   6.5781474 ,   6.5781474 ,   6.578152  ,
-         12.591245  ,  12.591249  ,  12.591251  ,  12.591253  ,
-         14.805855  ,  14.805855  ,  14.805863  ,  14.805869  ]])
+    expected_eigenvalues = np.array([[-31.007423  , -31.007412  , -20.67866   , -20.67866   ,
+         -7.1311283 ,  -7.1311274 ,  -6.2365704 ,  -6.236563  ,
+         -6.2365613 ,  -6.236559  ,  -4.480998  ,  -4.4809933 ,
+         -3.6357822 ,  -3.6357768 ,  -3.6357756 ,  -3.635771  ,
+         15.448533  ,  15.448537  ,  15.448545  ,  15.448548  ,
+         18.147615  ,  18.147623  ,  18.147627  ,  18.14763   ,
+         21.01311   ,  21.013113  ,  21.065922  ,  21.065931  ,
+         21.065935  ,  21.065937  ,  32.408985  ,  32.409     ,
+         32.412453  ,  32.412453  ,  32.41246   ,  32.412476  ],
+       [-30.019188  , -30.019176  , -23.397747  , -23.397734  ,
+         -8.958991  ,  -8.958984  ,  -8.785677  ,  -8.785676  ,
+         -6.2776294 ,  -6.2776294 ,  -3.6961977 ,  -3.69619   ,
+         -1.2760161 ,  -1.2760139 ,  -1.1798258 ,  -1.1798238 ,
+         13.354958  ,  13.354959  ,  14.130192  ,  14.130194  ,
+         16.96592   ,  16.965923  ,  17.039839  ,  17.03985   ,
+         21.02412   ,  21.024126  ,  21.045391  ,  21.045391  ,
+         24.641224  ,  24.641228  ,  29.585321  ,  29.585337  ,
+         29.58534   ,  29.585346  ,  30.32741   ,  30.32742   ],
+       [-27.350065  , -27.35006   , -27.350042  , -27.350033  ,
+        -10.221095  , -10.221094  , -10.221093  , -10.221088  ,
+         -5.289955  ,  -5.2899523 ,  -5.289951  ,  -5.28995   ,
+          0.7171254 ,   0.71712595,   0.71712595,   0.7171319 ,
+         14.130192  ,  14.130204  ,  14.130204  ,  14.1302185 ,
+         14.374711  ,  14.374722  ,  14.374723  ,  14.374728  ,
+         20.969217  ,  20.969225  ,  20.969225  ,  20.96923   ,
+         26.569042  ,  26.569044  ,  26.569046  ,  26.569054  ,
+         27.952032  ,  27.952042  ,  27.952045  ,  27.952055  ],
+       [-27.49865   , -27.498644  , -27.19673   , -27.19673   ,
+        -10.559897  , -10.55989   , -10.0144    , -10.014399  ,
+         -5.4945364 ,  -5.494536  ,  -4.606873  ,  -4.606865  ,
+          0.3039738 ,   0.30398044,   0.7527365 ,   0.7527418 ,
+         13.917292  ,  13.917292  ,  14.088804  ,  14.088809  ,
+         14.138776  ,  14.138779  ,  14.5281515 ,  14.528152  ,
+         21.21819   ,  21.218195  ,  21.557903  ,  21.557907  ,
+         25.775951  ,  25.775955  ,  26.720558  ,  26.72057   ,
+         27.603914  ,  27.603914  ,  28.495499  ,  28.495508  ],
+       [-27.919712  , -27.919712  , -26.734297  , -26.734297  ,
+        -11.013819  , -11.013816  ,  -9.459296  ,  -9.459282  ,
+         -5.9804435 ,  -5.980443  ,  -3.2812905 ,  -3.281289  ,
+         -0.75303936,  -0.75303817,   0.8340004 ,   0.83400106,
+         13.480661  ,  13.480672  ,  13.648575  ,  13.64858   ,
+         14.139681  ,  14.1396885 ,  14.928459  ,  14.928464  ,
+         21.759964  ,  21.759964  ,  23.02673   ,  23.026749  ,
+         24.617811  ,  24.61782   ,  26.638662  ,  26.638674  ,
+         27.167816  ,  27.167816  ,  29.041767  ,  29.041769  ],
+       [-28.098797  , -28.098768  , -26.521578  , -26.521578  ,
+        -10.925957  , -10.925954  ,  -9.719855  ,  -9.719849  ,
+         -5.3289404 ,  -5.3289347 ,  -3.4857965 ,  -3.4857874 ,
+         -0.8316949 ,  -0.8316835 ,   0.6037956 ,   0.60380006,
+         13.456583  ,  13.456588  ,  13.499516  ,  13.49952   ,
+         14.212908  ,  14.212912  ,  14.896738  ,  14.896739  ,
+         22.213148  ,  22.21315   ,  23.82316   ,  23.82316   ,
+         24.460823  ,  24.460823  ,  25.166416  ,  25.166416  ,
+         28.172297  ,  28.17231   ,  28.86319   ,  28.8632    ],
+       [-27.919712  , -27.919704  , -26.734303  , -26.7343    ,
+        -11.01382   , -11.013814  ,  -9.459293  ,  -9.459288  ,
+         -5.9804454 ,  -5.9804373 ,  -3.2812903 ,  -3.2812803 ,
+         -0.7530421 ,  -0.7530401 ,   0.8340004 ,   0.8340021 ,
+         13.480662  ,  13.480664  ,  13.648575  ,  13.648577  ,
+         14.139686  ,  14.139686  ,  14.928462  ,  14.928464  ,
+         21.759954  ,  21.759956  ,  23.026733  ,  23.02674   ,
+         24.617813  ,  24.617823  ,  26.638664  ,  26.638676  ,
+         27.167807  ,  27.167809  ,  29.041767  ,  29.041777  ],
+       [-29.944735  , -29.944721  , -23.561832  , -23.561829  ,
+         -9.715744  ,  -9.715741  ,  -7.5436654 ,  -7.5436587 ,
+         -6.1717205 ,  -6.1717124 ,  -4.739689  ,  -4.739689  ,
+         -1.834329  ,  -1.8343287 ,  -0.03966267,  -0.03966105,
+         13.234669  ,  13.234671  ,  14.016897  ,  14.016902  ,
+         16.121174  ,  16.121176  ,  16.95364   ,  16.953648  ,
+         22.364038  ,  22.364048  ,  22.465113  ,  22.465115  ,
+         23.00061   ,  23.00061   ,  29.137392  ,  29.137402  ,
+         29.942379  ,  29.942392  ,  29.959831  ,  29.959839  ],
+       [-31.007423  , -31.007412  , -20.67866   , -20.67866   ,
+         -7.1311283 ,  -7.1311274 ,  -6.2365704 ,  -6.236563  ,
+         -6.2365613 ,  -6.236559  ,  -4.480998  ,  -4.4809933 ,
+         -3.6357822 ,  -3.6357768 ,  -3.6357756 ,  -3.635771  ,
+         15.448533  ,  15.448537  ,  15.448545  ,  15.448548  ,
+         18.147615  ,  18.147623  ,  18.147627  ,  18.14763   ,
+         21.01311   ,  21.013113  ,  21.065922  ,  21.065931  ,
+         21.065935  ,  21.065937  ,  32.408985  ,  32.409     ,
+         32.412453  ,  32.412453  ,  32.41246   ,  32.412476  ],
+       [-30.286106  , -30.2861    , -22.779444  , -22.77944   ,
+         -8.85198   ,  -8.851977  ,  -7.2529488 ,  -7.2529426 ,
+         -6.6678324 ,  -6.6678243 ,  -3.990609  ,  -3.990604  ,
+         -3.436167  ,  -3.4361634 ,  -0.08859432,  -0.08859341,
+         13.929121  ,  13.929123  ,  13.930216  ,  13.93022   ,
+         17.052921  ,  17.052929  ,  17.060263  ,  17.060268  ,
+         22.167538  ,  22.167542  ,  22.193502  ,  22.193504  ,
+         22.477976  ,  22.477985  ,  30.144094  ,  30.144098  ,
+         30.552849  ,  30.552858  ,  30.55339   ,  30.553398  ],
+       [-29.173805  , -29.173784  , -25.033434  , -25.033417  ,
+         -9.945103  ,  -9.945093  ,  -7.7411456 ,  -7.7411284 ,
+         -7.1410484 ,  -7.141039  ,  -3.7853212 ,  -3.7853165 ,
+         -3.2343507 ,  -3.23435   ,   2.390824  ,   2.390826  ,
+         13.766307  ,  13.766312  ,  13.78635   ,  13.786352  ,
+         14.691134  ,  14.691136  ,  14.692142  ,  14.692142  ,
+         23.275118  ,  23.275133  ,  23.289207  ,  23.289211  ,
+         25.757067  ,  25.757072  ,  25.949417  ,  25.949417  ,
+         28.67229   ,  28.672306  ,  28.672436  ,  28.672441  ],
+       [-28.684181  , -28.684177  , -25.763704  , -25.7637    ,
+        -10.81131   , -10.811302  ,  -8.742746  ,  -8.742743  ,
+         -6.2349133 ,  -6.2349105 ,  -3.2419832 ,  -3.2419827 ,
+         -1.9287543 ,  -1.9287531 ,   1.3457793 ,   1.3457811 ,
+         13.380306  ,  13.380309  ,  13.471299  ,  13.471307  ,
+         14.535177  ,  14.535184  ,  15.065143  ,  15.065147  ,
+         22.488743  ,  22.488745  ,  23.84412   ,  23.844122  ,
+         24.985334  ,  24.985338  ,  25.403091  ,  25.403091  ,
+         28.17821   ,  28.178225  ,  29.002228  ,  29.002228  ],
+       [-27.351118  , -27.351114  , -27.341087  , -27.341082  ,
+        -10.680595  , -10.680593  , -10.367012  , -10.367     ,
+         -4.5969605 ,  -4.5969515 ,  -4.0144343 ,  -4.0144315 ,
+         -0.15337452,  -0.15337159,   0.09929406,   0.09930123,
+         13.607671  ,  13.607682  ,  13.630874  ,  13.630874  ,
+         14.311764  ,  14.311765  ,  14.321676  ,  14.321679  ,
+         22.750446  ,  22.75046   ,  22.754383  ,  22.754389  ,
+         24.868992  ,  24.868992  ,  24.869698  ,  24.8697    ,
+         28.487803  ,  28.487806  ,  28.497576  ,  28.497597  ],
+       [-27.351591  , -27.351583  , -27.344496  , -27.344492  ,
+        -10.500957  , -10.500956  , -10.2600565 , -10.260055  ,
+         -5.015775  ,  -5.0157743 ,  -4.6251984 ,  -4.625198  ,
+          0.30658934,   0.3065895 ,   0.4448485 ,   0.44485575,
+         13.87227   ,  13.872274  ,  13.888172  ,  13.888175  ,
+         14.319462  ,  14.319463  ,  14.325819  ,  14.325821  ,
+         21.738235  ,  21.738243  ,  21.74036   ,  21.74037   ,
+         25.797703  ,  25.797709  ,  25.79885   ,  25.798855  ,
+         28.278435  ,  28.278444  ,  28.286423  ,  28.286427  ],
+       [-27.350065  , -27.35006   , -27.350042  , -27.350033  ,
+        -10.221095  , -10.221094  , -10.221093  , -10.221088  ,
+         -5.289955  ,  -5.2899523 ,  -5.289951  ,  -5.28995   ,
+          0.7171254 ,   0.71712595,   0.71712595,   0.7171319 ,
+         14.130192  ,  14.130204  ,  14.130204  ,  14.1302185 ,
+         14.374711  ,  14.374722  ,  14.374723  ,  14.374728  ,
+         20.969217  ,  20.969225  ,  20.969225  ,  20.96923   ,
+         26.569042  ,  26.569044  ,  26.569046  ,  26.569054  ,
+         27.952032  ,  27.952042  ,  27.952045  ,  27.952055  ]],
+        dtype=np.float32)
+        
     
     assert np.allclose(eigenstatus["eigenvalues"], expected_eigenvalues, atol=1e-4)
 
diff --git a/dptb/tests/test_trainer.py b/dptb/tests/test_trainer.py
index a9b284c0..bcb178d3 100644
--- a/dptb/tests/test_trainer.py
+++ b/dptb/tests/test_trainer.py
@@ -2,7 +2,7 @@
 from dptb.nnops.trainer import Trainer
 import os
 from pathlib import Path
-from dptb.utils.argcheck import normalize
+from dptb.utils.argcheck import normalize,collect_cutoffs
 from dptb.utils.tools import j_loader
 from dptb.nn.build import build_model
 from dptb.data.build import build_dataset
@@ -24,7 +24,8 @@ class TestTrainer:
     
     jdata = j_loader(INPUT_file)
     jdata = normalize(jdata)
-    train_datasets = build_dataset(**jdata["data_options"]["train"], **jdata["common_options"])
+    cutoffops = collect_cutoffs(jdata)
+    train_datasets = build_dataset(**cutoffops, **jdata["data_options"]["train"], **jdata["common_options"])
 
 
     
@@ -70,7 +71,7 @@ def test_fromscratch_ref_noval(self):
         jdata["train_options"]["loss_options"]["reference"] = jdata["train_options"]["loss_options"]["train"]
         train_datasets = self.train_datasets
 
-        reference_datasets = build_dataset(**jdata["data_options"]["reference"], **jdata["common_options"])
+        reference_datasets = build_dataset(**self.cutoffops,**jdata["data_options"]["reference"], **jdata["common_options"])
 
         model = build_model(None, model_options=jdata["model_options"], 
                         common_options=jdata["common_options"])
@@ -97,7 +98,7 @@ def test_fromscratch_noref_val(self):
         jdata["train_options"]["loss_options"]["validation"] = jdata["train_options"]["loss_options"]["train"]
         train_datasets = self.train_datasets
 
-        validation_datasets = build_dataset(**jdata["data_options"]["validation"], **jdata["common_options"])
+        validation_datasets = build_dataset(**self.cutoffops,**jdata["data_options"]["validation"], **jdata["common_options"])
 
         model = build_model(None, model_options=jdata["model_options"], 
                         common_options=jdata["common_options"])
diff --git a/dptb/utils/argcheck.py b/dptb/utils/argcheck.py
index 4e138f56..c64a93a1 100644
--- a/dptb/utils/argcheck.py
+++ b/dptb/utils/argcheck.py
@@ -1,6 +1,8 @@
-from typing import List, Callable
+from typing import List, Callable, Dict, Any, Union
 from dargs import dargs, Argument, Variant, ArgumentEncoder
 import logging
+from numbers import Number
+
 
 log = logging.getLogger(__name__)
 
@@ -338,6 +340,9 @@ def test_data_sub():
 
 def data_options():
     args = [
+            Argument("r_max", [float,int], optional=True, default="5.0", doc="r_max"),
+            Argument("oer_max", [float,int], optional=True, default="5.0", doc="oer_max"),
+            Argument("er_max", [float,int], optional=True, default="5.0", doc="er_max"),
             train_data_sub(),
             validation_data_sub(),
             reference_data_sub()
@@ -594,6 +599,7 @@ def dftbsk():
 
     return Argument("dftbsk", dict, sub_fields=[
                 Argument("skdata", str, optional=False, doc="The path to the skfile or sk database."),
+                Argument("r_max", float, optional=False, doc="the cutoff values to use sk files."),
                 ], sub_variants=[], optional=True, doc=doc_dftbsk)
 
 def nnsk():
@@ -1413,28 +1419,14 @@ def set_info_options():
     doc_nframes = "Number of frames in this trajectory."
     doc_natoms = "Number of atoms in each frame."
     doc_pos_type = "Type of atomic position input. Can be frac / cart / ase."
+    doc_pbc = "The periodic condition for the structure, can bool or list of bool to specific x,y,z direction."
 
     args = [
         Argument("nframes", int, optional=False, doc=doc_nframes),
         Argument("natoms", int, optional=True, default=-1, doc=doc_natoms),
         Argument("pos_type", str, optional=False, doc=doc_pos_type),
-        bandinfo_sub(),
-        AtomicData_options_sub()
-    ]
-
-    return Argument("setinfo", dict, sub_fields=args)
-
-def set_info_options():
-    doc_nframes = "Number of frames in this trajectory."
-    doc_natoms = "Number of atoms in each frame."
-    doc_pos_type = "Type of atomic position input. Can be frac / cart / ase."
-
-    args = [
-        Argument("nframes", int, optional=False, doc=doc_nframes),
-        Argument("natoms", int, optional=True, default=-1, doc=doc_natoms),
-        Argument("pos_type", str, optional=False, doc=doc_pos_type),
-        bandinfo_sub(),
-        AtomicData_options_sub()
+        Argument("pbc", [bool, list], optional=False, doc=doc_pbc),
+        bandinfo_sub()
     ]
 
     return Argument("setinfo", dict, sub_fields=args)
@@ -1461,4 +1453,139 @@ def normalize_lmdbsetinfo(data):
     data = setinfo.normalize_value(data)
     setinfo.check_value(data, strict=True)
 
-    return data
\ No newline at end of file
+    return data
+
+
+def format_cuts(rcut: Union[Dict[str, Number], Number], decay_w: Number, nbuffer: int) -> Union[Dict[str, Number], Number]:
+    if not isinstance(decay_w, Number) or decay_w <= 0:
+        raise ValueError("decay_w should be a positive number")
+
+    buffer_addition = decay_w * nbuffer
+
+    if isinstance(rcut, dict):
+        return {key: value + buffer_addition for key, value in rcut.items()}
+    elif isinstance(rcut, Number):
+        return rcut + buffer_addition
+    else:
+        raise TypeError("rcut should be a dict or a number")
+
+def get_cutoffs_from_model_options(model_options):
+    """
+    Extract cutoff values from the provided model options.
+
+    This function retrieves the cutoff values `r_max`, `er_max`, and `oer_max` from the `model_options` 
+    dictionary. It handles different model types such as `embedding`, `nnsk`, and `dftbsk`, ensuring 
+    that the appropriate cutoff values are provided and valid.
+
+    Parameters:
+    model_options (dict): A dictionary containing model configuration options. It may include keys 
+                          like `embedding`, `nnsk`, and `dftbsk` with their respective cutoff values.
+
+    Returns:
+    tuple: A tuple containing the cutoff values (`r_max`, `er_max`, `oer_max`).
+
+    Raises:
+    ValueError: If neither `r_max` nor `rc` is provided in `model_options` for embedding.
+    AssertionError: If `r_max` is provided outside the `nnsk` or `dftbsk` context when those models are used.
+
+    Logs:
+    Error messages if required cutoff values are missing or incorrectly provided.
+    """
+    r_max, er_max, oer_max = None, None, None
+    if model_options.get("embedding",None) is not None:
+        # switch according to the embedding method
+        embedding = model_options.get("embedding")
+        if embedding["method"] == "se2":
+            er_max = embedding["rc"]
+        elif embedding["method"] in ["slem", "lem"]:
+            r_max = embedding["r_max"]
+        else:
+            log.error("The method of embedding have not been defined in get cutoff functions")
+            raise NotImplementedError("The method of embedding have not been defined in get cutoff functions")
+        
+    if model_options.get("nnsk", None) is not None:
+        assert r_max is None, "r_max should not be provided in outside the nnsk for training nnsk model."
+        if model_options["nnsk"]["hopping"].get("rs",None) is not None:
+            # 其他方法在模型公式中，已经包含了 +5w 的范围，所以这里为了保险额外加上3w 的范围; 
+            # 对于两个特例，powerlaw 和 varTang96 的情况，为了和旧版存档的兼容, 模型公式的本身并没有 +5w 的范围，所以这里额外加上8w 的范围。
+            if model_options["nnsk"]["hopping"]['method'] in ["powerlaw","varTang96"]:
+                # r_max = model_options["nnsk"]["hopping"]["rs"] + 8 * model_options["nnsk"]["hopping"]["w"]
+                r_max = format_cuts(model_options["nnsk"]["hopping"]["rs"], model_options["nnsk"]["hopping"]["w"], 8)
+            else:
+                # r_max = model_options["nnsk"]["hopping"]["rs"] + 3 * model_options["nnsk"]["hopping"]["w"]
+                r_max = format_cuts(model_options["nnsk"]["hopping"]["rs"], model_options["nnsk"]["hopping"]["w"], 3)
+
+        if model_options["nnsk"]["onsite"].get("rs",None) is not None:
+            if  model_options["nnsk"]["onsite"]['method'] == "strain" and model_options["nnsk"]["hopping"]['method'] in ["powerlaw","varTang96"]:
+                # oer_max = model_options["nnsk"]["onsite"]["rs"] + 8 * model_options["nnsk"]["onsite"]["w"]
+                oer_max = format_cuts(model_options["nnsk"]["onsite"]["rs"], model_options["nnsk"]["onsite"]["w"], 8)
+            else:
+                # oer_max = model_options["nnsk"]["onsite"]["rs"] + 3 * model_options["nnsk"]["onsite"]["w"]
+                oer_max = format_cuts(model_options["nnsk"]["onsite"]["rs"], model_options["nnsk"]["onsite"]["w"], 3)
+    
+    elif model_options.get("dftbsk", None) is not None:
+        assert r_max is None, "r_max should not be provided in outside the dftbsk for training dftbsk model."
+        r_max = model_options["dftbsk"]["r_max"]
+    
+    else:
+        # not nnsk not dftbsk, must be only env or E3. the embedding should be provided.
+        assert model_options.get("embedding",None) is not None   
+
+    return r_max, er_max, oer_max
+def collect_cutoffs(jdata):
+    """
+    Collect cutoff values from the provided JSON data.
+
+    This function extracts the cutoff values `r_max`, `er_max`, and `oer_max` from the `model_options` 
+    in the provided JSON data. If the `nnsk` push model is used, it ensures that the necessary 
+    cutoff values are provided in `data_options` and overrides the values from `model_options` 
+    accordingly.
+
+    Parameters:
+    jdata (dict): A dictionary containing model and data options. It must include `model_options` 
+                  and optionally `data_options` if `nnsk` push model is used.
+
+    Returns:
+    dict: A dictionary containing the cutoff options with keys `r_max`, `er_max`, and `oer_max`.
+
+    Raises:
+    AssertionError: If required keys are missing in `jdata` or if `r_max` is not provided when 
+                    using the `nnsk` push model.
+
+    Logs:
+    Various informational messages about the cutoff values and their sources.
+    """ 
+
+    model_options = jdata["model_options"]
+    r_max, er_max, oer_max = get_cutoffs_from_model_options(model_options)
+
+    if model_options.get("nnsk", None) is not None:
+        if model_options["nnsk"]["push"]:
+            assert jdata.get("data_options",None) is not None, "data_options should be provided in jdata for nnsk push"
+            assert jdata['data_options'].get("r_max") is not None, "r_max should be provided in data_options for nnsk push"
+            log.info('YOU ARE USING NNSK PUSH MODEL, r_max will be used from data_options. Be careful! check the value in data options and model options. r_max or rs/rc !')
+            r_max = jdata['data_options']['r_max']
+
+            if model_options["nnsk"]["onsite"]["method"] in ["strain", "NRL"]:
+                assert jdata['data_options'].get("oer_max") is not None, "oer_max should be provided in data_options for nnsk push with strain onsite mode"
+                log.info('YOU ARE USING NNSK PUSH MODEL with `strain` onsite mode, oer_max will be used from data_options. Be careful! check the value in data options and model options. rs/rc !')
+                oer_max = jdata['data_options']['oer_max']
+
+            if jdata['data_options'].get("er_max") is not None:
+                log.info("IN PUSH mode, the env correction should not be used. the er_max will not take effect.")     
+        else:
+            if  jdata['data_options'].get("r_max") is not None:
+                log.info("When not nnsk/push. the cutoffs will take from the model options: r_max  rs and rc values. this seting in data_options will be ignored.")
+
+    assert r_max is not None
+    cutoff_options = ({"r_max": r_max, "er_max": er_max, "oer_max": oer_max})
+    
+    log.info("-"*66)
+    log.info('     {:<55}    '.format("Cutoff options:"))
+    log.info('     {:<55}    '.format(" "*30))
+    log.info('     {:<16} : {:<36}    '.format("r_max", f"{r_max}"))
+    log.info('     {:<16} : {:<36}    '.format("er_max", f"{er_max}"))
+    log.info('     {:<16} : {:<36}    '.format("oer_max", f"{oer_max}"))
+    log.info("-"*66)
+
+    return cutoff_options
diff --git a/examples/hBN/band_plot.ipynb b/examples/hBN/band_plot.ipynb
index 4ba7b241..f0a7e94e 100644
--- a/examples/hBN/band_plot.ipynb
+++ b/examples/hBN/band_plot.ipynb
@@ -4,7 +4,15 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TBPLaS is not installed. Thus the TBPLaS is not available, Please install it first.\n"
+     ]
+    }
+   ],
    "source": [
     "from dptb.postprocess.bandstructure.band import Band\n",
     "from dptb.nn.nnsk import NNSK\n",
@@ -15,12 +23,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 450x400 with 1 Axes>"
       ]
@@ -36,21 +44,311 @@
     "results_path = \"./band_plot\"\n",
     "kpath_kwargs = jdata[\"task_options\"]\n",
     "stru_data = \"./data/struct.vasp\"\n",
-    "AtomicData_options = {\"r_max\": 5.0, \"oer_max\":1.6, \"pbc\": True}\n",
+    "AtomicData_options = {\"r_max\": 3.6+5*0.3}\n",
     "\n",
     "bcal = Band(model=model, \n",
     "            use_gui=False, \n",
     "            results_path=results_path, \n",
     "            device=model.device)\n",
     "bcal.get_bands(data=stru_data, \n",
-    "               kpath_kwargs=kpath_kwargs, \n",
-    "               AtomicData_options=AtomicData_options)\n",
+    "               kpath_kwargs=kpath_kwargs)\n",
     "bcal.band_plot(ref_band = kpath_kwargs[\"ref_band\"],\n",
     "               E_fermi = kpath_kwargs[\"E_fermi\"],\n",
     "               emin = kpath_kwargs[\"emin\"],\n",
     "               emax = kpath_kwargs[\"emax\"])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'nnsk': {'onsite': {'method': 'none'},\n",
+       "  'hopping': {'method': 'powerlaw', 'rs': 1.6, 'w': 0.3},\n",
+       "  'soc': {},\n",
+       "  'freeze': False,\n",
+       "  'push': False,\n",
+       "  'std': 0.01}}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.model_options"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.model_options \n",
+    "aa = {\n",
+    "    'pbc':None\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "aa.pop(\"pbc\",True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " ____            ____ _____ ____  \n",
+      "|  _ \\  ___  ___|  _ \\_   _| __ ) \n",
+      "| | | |/ _ \\/ _ \\ |_) || | |  _ \\ \n",
+      "| |_| |  __/  __/  __/ | | | |_) |\n",
+      "|____/ \\___|\\___|_|    |_| |____/ \n",
+      "                                  \n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyfiglet\n",
+    "\n",
+    "# 创建 Figlet 对象并设置字体\n",
+    "f = pyfiglet.Figlet(font='standard')\n",
+    "\n",
+    "# 将普通文本转换为艺术字并打印\n",
+    "text = \"DeePTB\"\n",
+    "print(f.renderText(text))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['univers', 'small_poison', 'clb8x8', 'stellar', 'icl-1900', 'skateord', 'pod_____', 'funky_dr', 'grand_pr', 'sansi', 'script__', 'rot13', 'amc_razor2', 'tec_7000', 'britei', 'bulbhead', 'arrows', 'rally_sp', 'chiseled', 'rozzo', 'mnemonic', 'utopiab', 'isometric1', 'ansi_regular', 'delta_corps_priest_1', 'xttyb', 'speed', 'xchartri', 'runyc', 'tanja', 'gothic__', 'chunky', 'maxfour', 'sl_script', '5x8', 'knob', 'xsbook', 'twopoint', 'sub-zero', 'demo_2__', 'char1___', 'advenger', 'ebbs_2__', 'test1', 'xhelvbi', 'serifcap', 'isometric2', 'larry3d', 'amc_neko', 'c_ascii_', 'radical_', 'helvb', 'fbr12___', 'sblood', 'train', 'js_bracket_letters', 'jazmine', 'graffiti', 'rally_s2', 'glenyn', 'coil_cop', 'rev', 'dcs_bfmo', 'big_money-sw', 'clr5x6', 'smslant', 'a_zooloo', 'eftiwater', 'space_op', 'the_edge', 'asslt__m', 'triad_st', 'timesofl', 'basic', 'e__fist_', 'fireing_', 'clr8x8', 'dotmatrix', 'star_strips', 'f15_____', 'amc_3_liv1', 'cybermedium', 'pawn_ins', 'nancyj-underlined', 'rastan__', 'lean', 'tecrvs__', 'future_3', 'pepper', 'taxi____', 'skate_ro', 'danc4', 'nfi1____', 'lockergnome', 'elite', 'ogre', 'bigfig', 'benjamin', 'cola', 'bloody', 'ttyb', 'charact3', 'big_money-nw', 'keyboard', 'heart_right', 'rainbow_', 'britebi', 'banner3-D', 'sbooki', 'demo_1__', 'amc_3_line', 'mad_nurs', 'platoon_', 'binary', 'invita', 'hades___', 'smshadow', 'crawford2', 'tsn_base', 'top_duck', 'rammstein', 'cosmike', 'bubble', 'road_rai', 'sbookbi', 'notie_ca', 'heavy_me', 'z-pilot_', 't__of_ap', 'horizontal_right', 'clr6x6', 'relief2', 'asc_____', 'krak_out', 'fair_mea', 'fp2_____', 'thin', 'flower_power', 'this', 'deep_str', 'old_banner', 'eca_____', 'master_o', 'stop', 'ebbs_1__', 'morse2', 'ansi_shadow', 'sweet', 'bubble_b', 'impossible', 'sm______', 'platoon2', 'epic', 'xhelvi', 'star_war', 'courbi', 'future_6', 'rampage_', '6x10', 'future_8', 'small_caps', '1943____', 'big_money-ne', 'js_cursive', 'times', 'flipped', 'courb', 'alligator2', '1row', 'hills___', 'finalass', 'line_blocks', 'hyper___', 'linux', 'hex', 'slscript', 'red_phoenix', 'subteran', 'couri', 'ghost', 'gauntlet', 'short', 'poison', 'smisome1', 'konto', 'xcouri', 'morse', 'fender', 'dos_rebel', 'double', 'cyberlarge', 'home_pak', 'octal', 'kban', 'hieroglyphs', 'contessa', 'clr7x10', 'banner', 'katakana', 'letterw3', 'wavy', 'fp1_____', 'bell', 'nvscript', 'zone7___', 'com_sen_', 'stacey', 'letters', 'fun_face', 'eftifont', 'fbr1____', 'magic_ma', 'marquee', 'lil_devil', 'ghoulish', 'amc_thin', 'isometric4', 'letter_w', 'shadow', 'fuzzy', 'nipples', 'eftipiti', 'outrun__', 'block', 'street_s', 'chartri', 'd_dragon', 'sbookb', 'spc_demo', 'gothic', 'rci_____', 'os2', 'jacky', 'xcourb', 'crawford', 'cybersmall', 'demo_m__', 'threepoint', 'war_of_w', 'banner3', 'tengwar', 'new_asci', 'xcourbi', 'eftichess', 'caligraphy', 'tomahawk', 'slant', 'xsansbi', 'merlin2', 'briteb', 'b_m__200', 'stick_letters', 'clr6x10', 'dancing_font', 'soft', 'acrobatic', 'wet_letter', 'fbr_stri', 'banner4', 'yie-ar__', 'ntgreek', 'patorjk-hex', 'future_1', 'amc_tubes', 'puzzle', 'bear', 'small_slant', 'calvin_s', 'trek', 'atc_____', 'super_te', 'panther_', 'italic', 'ascii___', 'colossal', 'pacos_pe', 'xchartr', 'future_5', 'fantasy_', 'fbr_tilt', 'tiles', 'rad_phan', 'tav1____', 'flyn_sh', 'tsalagi', 'ripper!_', 'small', 'puffy', 'xsbookbi', 'stronger_than_all', 'brite', 'kik_star', 'battlesh', 'aquaplan', 'rowancap', 'ts1_____', 'type_set', '4max', 'horizontal_left', 'eftiwall', 'odel_lak', 'def_leppard', 'stforek', 'bigchief', 'fun_faces', 'swan', 'xhelvb', 'vortron_', 'house_of', 'xcour', 'sansb', 'calgphy2', 'cursive', 'lcd', 'mike', 'roman', 'charact5', 'xbritebi', 'catwalk', 'assalt_m', 'hypa_bal', 'broadway', 'char3___', 'chartr', 'beer_pub', 'battle_s', 'small_shadow', 'smkeyboard', 'filter', 'convoy__', 'runic', 'thick', 'merlin1', 'crazy', 'future_2', 'rotated', 'high_noo', 'electronic', 'heart_left', 'helv', 'fbr2____', 'phonix__', 'pebbles', 'xsansi', 'pawp', 'amc_slash', 'unarmed_', 'jerusalem', 'spliff', 'slant_relief', 'italics_', 'xhelv', 'relief', \"patorjk's_cheese\", 'tubular', 'cli8x8', 'fourtops', '3d-ascii', 'alligator', 'xtty', 'char4___', 'santa_clara', 'xbriteb', 'green_be', '64f1____', '4x4_offr', 'lazy_jon', 'weird', 'inc_raw_', 'amc_slider', 'amc_aaa01', 'doh', 'avatar', 'b1ff', 'caus_in_', 'mirror', 'yie_ar_k', 'contrast', 'fire_font-s', 'clr5x8', 'usa_____', 'hollywood', 'c1______', '6x9', 'tsm_____', 'rok_____', 'defleppard', 'atc_gran', 'shimrod', 'fairligh', 'decimal', 'computer', 'heroboti', 'joust___', 'alphabet', '5x7', '3d_diagonal', 'goofy', 'xbrite', 'clb6x10', 'eftirobot', 'nscript', 'js_block_letters', 'cricket', 'r2-d2___', 'sketch_s', 'stencil2', 'ghost_bo', 'georgi16', 'konto_slant', 'rounded', 'digital', 'xsansb', 'drpepper', 'c2______', 'helvi', 'varsity', 'xsbooki', 'xtimes', 'utopiabi', 'rad_____', 'eftitalic', 'straight', 'npn_____', 'mayhem_d', 'double_shorts', 'sbook', 'barbwire', 'stealth_', 'efti_robot', 'trashman', 'diamond', 'charact4', 'clr4x6', 'stampatello', 'ticks', 'js_stick_letters', 'future_4', 'ok_beer_', 'xbritei', 'big', 'future_7', 'cards', 'fraktur', 'usaflag', 'greek', 'peaks', 'graceful', 'etcrvs__', 'mig_ally', 'modern__', 'ugalympi', 'clr8x10', 'rectangles', 'cygnet', 'broadway_kb', 'henry_3d', 'cosmic', 'cour', 'term', 'clr5x10', 'whimsy', 'big_money-se', 'script', 'p_s_h_m_', 'bright', 'stencil1', 'charact1', 'clb8x10', 'swamp_land', 'dwhistled', '3-d', 'standard', 'raw_recu', 'ivrit', 'bubble__', 'nancyj', 'madrid', 'usa_pq__', 'nancyj-improved', 'faces_of', 'utopiai', 'characte', 'mini', 'wow', 'smtengwar', 'bolger', 'twisted', 'js_capital_curves', 'charact6', 'tinker-toy', 'coinstak', 'sansbi', 'tty', 'amc_untitled', 'char2___', 'xsans', 'ucf_fan_', 'skateroc', '3x5', 'nancyj-fancy', 'roman___', 'tec1____', 'clr7x8', 'devilish', 'alpha', 'mshebrew210', 'pyramid', 'stampate', 'gradient', 'p_skateb', 'fire_font-k', 'charset_', 'starwars', 'braced', 'ticksslant', 'georgia11', 'mcg_____', 'baz__bil', 'moscow', 'diet_cola', 'tombstone', 'kgames_i', 'clr6x8', 'lexible_', 'smscript', 'helvbi', 'muzzle', 'druid___', 'c_consen', 'doom', 'charact2', 'isometric3', 'xsbookb', 'zig_zag_', '5lineoblique', 'rockbox_', 'slide', 'ti_pan__', 'ascii_new_roman', 'twin_cob', 'utopia', 'blocky', 'modular', 'blocks', 'thorned', 'sans', 'o8', 'amc_razor']\n"
+     ]
+    }
+   ],
+   "source": [
+    "from pyfiglet import FigletFont\n",
+    "fonts_res = FigletFont().getFonts()\n",
+    "print(fonts_res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Font: dos_rebel\n",
+      " ██████████                     ███████████  ███████████ ███████████ \n",
+      "░░███░░░░███                   ░░███░░░░░███░█░░░███░░░█░░███░░░░░███\n",
+      " ░███   ░░███  ██████   ██████  ░███    ░███░   ░███  ░  ░███    ░███\n",
+      " ░███    ░███ ███░░███ ███░░███ ░██████████     ░███     ░██████████ \n",
+      " ░███    ░███░███████ ░███████  ░███░░░░░░      ░███     ░███░░░░░███\n",
+      " ░███    ███ ░███░░░  ░███░░░   ░███            ░███     ░███    ░███\n",
+      " ██████████  ░░██████ ░░██████  █████           █████    ███████████ \n",
+      "░░░░░░░░░░    ░░░░░░   ░░░░░░  ░░░░░           ░░░░░    ░░░░░░░░░░░  \n",
+      "                                                                     \n",
+      "                                                                     \n",
+      "                                                                     \n",
+      "\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Font: electronic\n",
+      " ▄▄▄▄▄▄▄▄▄▄   ▄▄▄▄▄▄▄▄▄▄▄  ▄▄▄▄▄▄▄▄▄▄▄  ▄▄▄▄▄▄▄▄▄▄▄  ▄▄▄▄▄▄▄▄▄▄▄  ▄▄▄▄▄▄▄▄▄▄  \n",
+      "▐░░░░░░░░░░▌ ▐░░░░░░░░░░░▌▐░░░░░░░░░░░▌▐░░░░░░░░░░░▌▐░░░░░░░░░░░▌▐░░░░░░░░░░▌ \n",
+      "▐░█▀▀▀▀▀▀▀█░▌▐░█▀▀▀▀▀▀▀▀▀ ▐░█▀▀▀▀▀▀▀▀▀ ▐░█▀▀▀▀▀▀▀█░▌ ▀▀▀▀█░█▀▀▀▀ ▐░█▀▀▀▀▀▀▀█░▌\n",
+      "▐░▌       ▐░▌▐░▌          ▐░▌          ▐░▌       ▐░▌     ▐░▌     ▐░▌       ▐░▌\n",
+      "▐░▌       ▐░▌▐░█▄▄▄▄▄▄▄▄▄ ▐░█▄▄▄▄▄▄▄▄▄ ▐░█▄▄▄▄▄▄▄█░▌     ▐░▌     ▐░█▄▄▄▄▄▄▄█░▌\n",
+      "▐░▌       ▐░▌▐░░░░░░░░░░░▌▐░░░░░░░░░░░▌▐░░░░░░░░░░░▌     ▐░▌     ▐░░░░░░░░░░▌ \n",
+      "▐░▌       ▐░▌▐░█▀▀▀▀▀▀▀▀▀ ▐░█▀▀▀▀▀▀▀▀▀ ▐░█▀▀▀▀▀▀▀▀▀      ▐░▌     ▐░█▀▀▀▀▀▀▀█░▌\n",
+      "▐░▌       ▐░▌▐░▌          ▐░▌          ▐░▌               ▐░▌     ▐░▌       ▐░▌\n",
+      "▐░█▄▄▄▄▄▄▄█░▌▐░█▄▄▄▄▄▄▄▄▄ ▐░█▄▄▄▄▄▄▄▄▄ ▐░▌               ▐░▌     ▐░█▄▄▄▄▄▄▄█░▌\n",
+      "▐░░░░░░░░░░▌ ▐░░░░░░░░░░░▌▐░░░░░░░░░░░▌▐░▌               ▐░▌     ▐░░░░░░░░░░▌ \n",
+      " ▀▀▀▀▀▀▀▀▀▀   ▀▀▀▀▀▀▀▀▀▀▀  ▀▀▀▀▀▀▀▀▀▀▀  ▀                 ▀       ▀▀▀▀▀▀▀▀▀▀  \n",
+      "                                                                              \n",
+      "\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Font: varsity\n",
+      " ______                  _______   _________  ______    \n",
+      "|_   _ `.               |_   __ \\ |  _   _  ||_   _ \\   \n",
+      "  | | `. \\ .---.  .---.   | |__) ||_/ | | \\_|  | |_) |  \n",
+      "  | |  | |/ /__\\\\/ /__\\\\  |  ___/     | |      |  __'.  \n",
+      " _| |_.' /| \\__.,| \\__., _| |_       _| |_    _| |__) | \n",
+      "|______.'  '.__.' '.__.'|_____|     |_____|  |_______/  \n",
+      "                                                        \n",
+      "\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Font: blocky\n",
+      "████████  ████████ ████████ ████████  ████████ ████████  \n",
+      "██     ██ ██       ██       ██     ██    ██    ██     ██ \n",
+      "██     ██ ██       ██       ██     ██    ██    ██     ██ \n",
+      "██     ██ ██████   ██████   ████████     ██    ████████  \n",
+      "██     ██ ██       ██       ██           ██    ██     ██ \n",
+      "██     ██ ██       ██       ██           ██    ██     ██ \n",
+      "████████  ████████ ████████ ██           ██    ████████  \n",
+      "\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Font: standard\n",
+      " ____            ____ _____ ____  \n",
+      "|  _ \\  ___  ___|  _ \\_   _| __ ) \n",
+      "| | | |/ _ \\/ _ \\ |_) || | |  _ \\ \n",
+      "| |_| |  __/  __/  __/ | | | |_) |\n",
+      "|____/ \\___|\\___|_|    |_| |____/ \n",
+      "                                  \n",
+      "\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Font: doom\n",
+      "______         ______ ___________ \n",
+      "|  _  \\        | ___ \\_   _| ___ \\\n",
+      "| | | |___  ___| |_/ / | | | |_/ /\n",
+      "| | | / _ \\/ _ \\  __/  | | | ___ \\\n",
+      "| |/ /  __/  __/ |     | | | |_/ /\n",
+      "|___/ \\___|\\___\\_|     \\_/ \\____/ \n",
+      "                                  \n",
+      "                                  \n",
+      "\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Font: roman\n",
+      "oooooooooo.                       ooooooooo.   ooooooooooooo oooooooooo.  \n",
+      "`888'   `Y8b                      `888   `Y88. 8'   888   `8 `888'   `Y8b \n",
+      " 888      888  .ooooo.   .ooooo.   888   .d88'      888       888     888 \n",
+      " 888      888 d88' `88b d88' `88b  888ooo88P'       888       888oooo888' \n",
+      " 888      888 888ooo888 888ooo888  888              888       888    `88b \n",
+      " 888     d88' 888    .o 888    .o  888              888       888    .88P \n",
+      "o888bood8P'   `Y8bod8P' `Y8bod8P' o888o            o888o     o888bood8P'  \n",
+      "                                                                          \n",
+      "                                                                          \n",
+      "                                                                          \n",
+      "\n",
+      "--------------------------------------------------\n",
+      "\n",
+      "Font: colossal\n",
+      "8888888b.                 8888888b.88888888888888888b.   \n",
+      "888  \"Y88b                888   Y88b   888    888  \"88b  \n",
+      "888    888                888    888   888    888  .88P  \n",
+      "888    888 .d88b.  .d88b. 888   d88P   888    8888888K.  \n",
+      "888    888d8P  Y8bd8P  Y8b8888888P\"    888    888  \"Y88b \n",
+      "888    8888888888888888888888          888    888    888 \n",
+      "888  .d88PY8b.    Y8b.    888          888    888   d88P \n",
+      "8888888P\"  \"Y8888  \"Y8888 888          888    8888888P\"  \n",
+      "                                                         \n",
+      "                                                         \n",
+      "                                                         \n",
+      "\n",
+      "--------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pyfiglet\n",
+    "#fonts = ['univers', 'small_poison', 'clb8x8', 'stellar', 'icl-1900', 'skateord', 'pod_____', 'funky_dr', 'grand_pr', 'sansi', 'script__', 'rot13', 'amc_razor2', 'tec_7000', 'britei', 'bulbhead', 'arrows', 'rally_sp', 'chiseled', 'rozzo', 'mnemonic', 'utopiab', 'isometric1', 'ansi_regular', 'delta_corps_priest_1', 'xttyb', 'speed', 'xchartri', 'runyc', 'tanja', 'gothic__', 'chunky', 'maxfour', 'sl_script', '5x8', 'knob', 'xsbook', 'twopoint', 'sub-zero', 'demo_2__', 'char1___', 'advenger', 'ebbs_2__', 'test1', 'xhelvbi', 'serifcap', 'isometric2', 'larry3d', 'amc_neko', 'c_ascii_', 'radical_', 'helvb', 'fbr12___', 'sblood', 'train', 'js_bracket_letters', 'jazmine', 'graffiti', 'rally_s2', 'glenyn', 'coil_cop', 'rev', 'dcs_bfmo', 'big_money-sw', 'clr5x6', 'smslant', 'a_zooloo', 'eftiwater', 'space_op', 'the_edge', 'asslt__m', 'triad_st', 'timesofl', 'basic', 'e__fist_', 'fireing_', 'clr8x8', 'dotmatrix', 'star_strips', 'f15_____', 'amc_3_liv1', 'cybermedium', 'pawn_ins', 'nancyj-underlined', 'rastan__', 'lean', 'tecrvs__', 'future_3', 'pepper', 'taxi____', 'skate_ro', 'danc4', 'nfi1____', 'lockergnome', 'elite', 'ogre', 'bigfig', 'benjamin', 'cola', 'bloody', 'ttyb', 'charact3', 'big_money-nw', 'keyboard', 'heart_right', 'rainbow_', 'britebi', 'banner3-D', 'sbooki', 'demo_1__', 'amc_3_line', 'mad_nurs', 'platoon_', 'binary', 'invita', 'hades___', 'smshadow', 'crawford2', 'tsn_base', 'top_duck', 'rammstein', 'cosmike', 'bubble', 'road_rai', 'sbookbi', 'notie_ca', 'heavy_me', 'z-pilot_', 't__of_ap', 'horizontal_right', 'clr6x6', 'relief2', 'asc_____', 'krak_out', 'fair_mea', 'fp2_____', 'thin', 'flower_power', 'this', 'deep_str', 'old_banner', 'eca_____', 'master_o', 'stop', 'ebbs_1__', 'morse2', 'ansi_shadow', 'sweet', 'bubble_b', 'impossible', 'sm______', 'platoon2', 'epic', 'xhelvi', 'star_war', 'courbi', 'future_6', 'rampage_', '6x10', 'future_8', 'small_caps', '1943____', 'big_money-ne', 'js_cursive', 'times', 'flipped', 'courb', 'alligator2', '1row', 'hills___', 'finalass', 'line_blocks', 'hyper___', 'linux', 'hex', 'slscript', 'red_phoenix', 'subteran', 'couri', 'ghost', 'gauntlet', 'short', 'poison', 'smisome1', 'konto', 'xcouri', 'morse', 'fender', 'dos_rebel', 'double', 'cyberlarge', 'home_pak', 'octal', 'kban', 'hieroglyphs', 'contessa', 'clr7x10', 'banner', 'katakana', 'letterw3', 'wavy', 'fp1_____', 'bell', 'nvscript', 'zone7___', 'com_sen_', 'stacey', 'letters', 'fun_face', 'eftifont', 'fbr1____', 'magic_ma', 'marquee', 'lil_devil', 'ghoulish', 'amc_thin', 'isometric4', 'letter_w', 'shadow', 'fuzzy', 'nipples', 'eftipiti', 'outrun__', 'block', 'street_s', 'chartri', 'd_dragon', 'sbookb', 'spc_demo', 'gothic', 'rci_____', 'os2', 'jacky', 'xcourb', 'crawford', 'cybersmall', 'demo_m__', 'threepoint', 'war_of_w', 'banner3', 'tengwar', 'new_asci', 'xcourbi', 'eftichess', 'caligraphy', 'tomahawk', 'slant', 'xsansbi', 'merlin2', 'briteb', 'b_m__200', 'stick_letters', 'clr6x10', 'dancing_font', 'soft', 'acrobatic', 'wet_letter', 'fbr_stri', 'banner4', 'yie-ar__', 'ntgreek', 'patorjk-hex', 'future_1', 'amc_tubes', 'puzzle', 'bear', 'small_slant', 'calvin_s', 'trek', 'atc_____', 'super_te', 'panther_', 'italic', 'ascii___', 'colossal', 'pacos_pe', 'xchartr', 'future_5', 'fantasy_', 'fbr_tilt', 'tiles', 'rad_phan', 'tav1____', 'flyn_sh', 'tsalagi', 'ripper!_', 'small', 'puffy', 'xsbookbi', 'stronger_than_all', 'brite', 'kik_star', 'battlesh', 'aquaplan', 'rowancap', 'ts1_____', 'type_set', '4max', 'horizontal_left', 'eftiwall', 'odel_lak', 'def_leppard', 'stforek', 'bigchief', 'fun_faces', 'swan', 'xhelvb', 'vortron_', 'house_of', 'xcour', 'sansb', 'calgphy2', 'cursive', 'lcd', 'mike', 'roman', 'charact5', 'xbritebi', 'catwalk', 'assalt_m', 'hypa_bal', 'broadway', 'char3___', 'chartr', 'beer_pub', 'battle_s', 'small_shadow', 'smkeyboard', 'filter', 'convoy__', 'runic', 'thick', 'merlin1', 'crazy', 'future_2', 'rotated', 'high_noo', 'electronic', 'heart_left', 'helv', 'fbr2____', 'phonix__', 'pebbles', 'xsansi', 'pawp', 'amc_slash', 'unarmed_', 'jerusalem', 'spliff', 'slant_relief', 'italics_', 'xhelv', 'relief', \"patorjk's_cheese\", 'tubular', 'cli8x8', 'fourtops', '3d-ascii', 'alligator', 'xtty', 'char4___', 'santa_clara', 'xbriteb', 'green_be', '64f1____', '4x4_offr', 'lazy_jon', 'weird', 'inc_raw_', 'amc_slider', 'amc_aaa01', 'doh', 'avatar', 'b1ff', 'caus_in_', 'mirror', 'yie_ar_k', 'contrast', 'fire_font-s', 'clr5x8', 'usa_____', 'hollywood', 'c1______', '6x9', 'tsm_____', 'rok_____', 'defleppard', 'atc_gran', 'shimrod', 'fairligh', 'decimal', 'computer', 'heroboti', 'joust___', 'alphabet', '5x7', '3d_diagonal', 'goofy', 'xbrite', 'clb6x10', 'eftirobot', 'nscript', 'js_block_letters', 'cricket', 'r2-d2___', 'sketch_s', 'stencil2', 'ghost_bo', 'georgi16', 'konto_slant', 'rounded', 'digital', 'xsansb', 'drpepper', 'c2______', 'helvi', 'varsity', 'xsbooki', 'xtimes', 'utopiabi', 'rad_____', 'eftitalic', 'straight', 'npn_____', 'mayhem_d', 'double_shorts', 'sbook', 'barbwire', 'stealth_', 'efti_robot', 'trashman', 'diamond', 'charact4', 'clr4x6', 'stampatello', 'ticks', 'js_stick_letters', 'future_4', 'ok_beer_', 'xbritei', 'big', 'future_7', 'cards', 'fraktur', 'usaflag', 'greek', 'peaks', 'graceful', 'etcrvs__', 'mig_ally', 'modern__', 'ugalympi', 'clr8x10', 'rectangles', 'cygnet', 'broadway_kb', 'henry_3d', 'cosmic', 'cour', 'term', 'clr5x10', 'whimsy', 'big_money-se', 'script', 'p_s_h_m_', 'bright', 'stencil1', 'charact1', 'clb8x10', 'swamp_land', 'dwhistled', '3-d', 'standard', 'raw_recu', 'ivrit', 'bubble__', 'nancyj', 'madrid', 'usa_pq__', 'nancyj-improved', 'faces_of', 'utopiai', 'characte', 'mini', 'wow', 'smtengwar', 'bolger', 'twisted', 'js_capital_curves', 'charact6', 'tinker-toy', 'coinstak', 'sansbi', 'tty', 'amc_untitled', 'char2___', 'xsans', 'ucf_fan_', 'skateroc', '3x5', 'nancyj-fancy', 'roman___', 'tec1____', 'clr7x8', 'devilish', 'alpha', 'mshebrew210', 'pyramid', 'stampate', 'gradient', 'p_skateb', 'fire_font-k', 'charset_', 'starwars', 'braced', 'ticksslant', 'georgia11', 'mcg_____', 'baz__bil', 'moscow', 'diet_cola', 'tombstone', 'kgames_i', 'clr6x8', 'lexible_', 'smscript', 'helvbi', 'muzzle', 'druid___', 'c_consen', 'doom', 'charact2', 'isometric3', 'xsbookb', 'zig_zag_', '5lineoblique', 'rockbox_', 'slide', 'ti_pan__', 'ascii_new_roman', 'twin_cob', 'utopia', 'blocky', 'modular', 'blocks', 'thorned', 'sans', 'o8', 'amc_razor']\n",
+    "\n",
+    "def display_deeptb_fonts():\n",
+    "    fonts = ['dos_rebel', 'electronic', 'varsity', 'blocky', 'standard', 'doom', 'roman', 'colossal']\n",
+    "    text = \"DeePTB\"\n",
+    "    \n",
+    "    for font in fonts:\n",
+    "        print(f\"\\nFont: {font}\")\n",
+    "        f = pyfiglet.Figlet(font=font)\n",
+    "        print(f.renderText(text))\n",
+    "        print(\"-\" * 50)\n",
+    "\n",
+    "# 显示所有字体\n",
+    "display_deeptb_fonts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "padding=2\n",
+    "nnn = 78-padding\n",
+    "format_str = \"#\" + \"{}\"+\"{:<\"+f\"{nnn}\" + \"}\"+ \"#\"      "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "66"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(\"##################################################################\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/examples/hBN/data/kpath.0/info.json b/examples/hBN/data/kpath.0/info.json
index b48038b6..8912eaf7 100644
--- a/examples/hBN/data/kpath.0/info.json
+++ b/examples/hBN/data/kpath.0/info.json
@@ -2,12 +2,7 @@
     "nframes": 1,
     "natoms": 2,
     "pos_type": "ase",
-    "AtomicData_options": {
-        "r_max": 5.5,
-        "er_max": 3.5,
-        "oer_max":1.6,
-        "pbc": true
-    },
+    "pbc": true,
     "bandinfo": {
         "band_min": 0,
         "band_max": 6,
diff --git a/examples/hBN/input_short.json b/examples/hBN/input_short.json
index ce1f0330..8eb1b882 100644
--- a/examples/hBN/input_short.json
+++ b/examples/hBN/input_short.json
@@ -37,6 +37,9 @@
         }
     },
     "data_options": {
+        "r_max": 5.5,
+        "er_max": 3.5,
+        "oer_max":1.6,
         "train": {
             "root": "./data/",
             "prefix": "kpath",