BatchGenerate
An introduction to the functions in /BatchGenerate
CheckStructures
(
batch_structures: <class 'BM4Ckit.BatchStructures.BatchStructuresBase.BatchStructures'>
)
check_distance
(
radius_threshold: <class 'float'> = 1
)
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check atomic pairwise distances that are too close (less than given `radius_threshold`)
Args:
radius_threshold: float, the threshold radius.
Returns:
mask_array: ndarray[(n_batch, ), bool], where False for failed to the check.
check_surf_smoothness
(
smoothness_threshold: <class 'float'> = 0.5
r_cutoff: <class 'float'> = 3.0
surf_check_method: Optional[Literal['ConnectedGraph', 'C', 'MinDistance', 'M']] = MinDistance
)
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check whether the surface smooth enough (whether the max difference among surface atoms along z axis < `smoothness_threshold`)
Args:
smoothness_threshold: smoothness threshold.
r_cutoff: the cut-off distance to form the graph or inter-atomic distance checking.
surf_check_method: method of surface checking.
None: no check will applied;
'ConnectedGraph' or 'C': it will check whether the surface atoms form only one connected graph. If not, mask returns False.
'MinDistance' or 'M': it will check for atoms whose shortest distance from other atoms exceeds the cut-off distance.
Returns:
mask_array: ndarray[(n_batch, ), bool], where False for failed to the check.
InitAdsStructure
(
slabs: <class 'BM4Ckit.BatchStructures.BatchStructuresBase.BatchStructures'>
ads: <class 'torch.Tensor'>
ads_elements: Dict
vaccuum_direction: Literal['x', 'y', 'z'] = z
device: str | torch.device = cpu
verbose: <class 'int'> = 0
)
None
run
(
slab_site: List[List[int]]
ads_site: List[int]
bond_length: <class 'float'>
bond_coeff: <class 'float'> = 5.0
non_bond_coeff: <class 'float'> = 3.0
keep_ads_conf_coeff: <class 'float'> = 2.0
)
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Args:
slab_site: The indices of adsorption site in slabs.
ads_site: The indices of adsorption site in adsorbate.
bond_length: The target bond length of adsorbate and slabs.
bond_coeff: Strength coefficient of ads-slab bond.
non_bond_coeff: Strength coefficient of repulsiveness of non-bond atom between ads. and slab.
keep_ads_conf_coeff: Strength coefficient of keeping original adsorbate configuration.
Returns:
BatchStructures: the batch structures of slabs with ads.
RandReplace
(
size: <class 'int'> = 1
seed: <class 'int'> = None
device: str | torch.device = cpu
verbose: <class 'int'> = 2
)
run
(
X: <class 'torch.Tensor'>
Element_list: Union[List[List[str]], List[List[int]]]
replace_element_range: Dict[str | int, Tuple[str | int]]
replace_numbers: Sequence[int]
batch_indices: Union[Sequence[int], torch.Tensor, numpy.ndarray, NoneType] = None
)
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Args:
X: Tensor[n_batch, n_atom, 3], the atom coordinates.
Element_list: List[List[str | int]], the atomic type (element) corresponding to each row of each batch in X.
replace_element_range: dict((symbol of element to be replaced | index of atom in X) to be replaced : tuple(symbol | atomic number of elements to replace))
replace_numbers: the number of times that each atom is replaced. It must have the same length of `replace_element_range`.
batch_indices: Sequence | th.Tensor | np.ndarray | None, the split points for given X, Element_list & V_init, must be 1D integer array_like.
the format of batch_indices is same as `split_size_or_sections` in torch.split:
batch_indices = (n1, n2, ..., nN) will split X, Element_list & V_init into N parts, and ith parts has ni atoms. sum(n1, ..., nN) = X.shape[1]
Returns: