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# SPDX-License-Identifier: LGPL-3.0-or-later | ||
from deepmd.infer.deep_tensor import ( | ||
DeepTensor, | ||
) | ||
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class DeepDipole(DeepTensor): | ||
"""Deep dipole model. | ||
Parameters | ||
---------- | ||
model_file : Path | ||
The name of the frozen model file. | ||
*args : list | ||
Positional arguments. | ||
auto_batch_size : bool or int or AutoBatchSize, default: True | ||
If True, automatic batch size will be used. If int, it will be used | ||
as the initial batch size. | ||
neighbor_list : ase.neighborlist.NewPrimitiveNeighborList, optional | ||
The ASE neighbor list class to produce the neighbor list. If None, the | ||
neighbor list will be built natively in the model. | ||
**kwargs : dict | ||
Keyword arguments. | ||
""" | ||
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||
@property | ||
def output_tensor_name(self) -> str: | ||
return "dipole" |
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# SPDX-License-Identifier: LGPL-3.0-or-later | ||
from typing import ( | ||
Any, | ||
Dict, | ||
List, | ||
Optional, | ||
Tuple, | ||
Union, | ||
) | ||
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import numpy as np | ||
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from deepmd.dpmodel.output_def import ( | ||
FittingOutputDef, | ||
ModelOutputDef, | ||
OutputVariableDef, | ||
) | ||
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from .deep_eval import ( | ||
DeepEval, | ||
) | ||
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class DeepDOS(DeepEval): | ||
"""Deep density of states model. | ||
Parameters | ||
---------- | ||
model_file : Path | ||
The name of the frozen model file. | ||
*args : list | ||
Positional arguments. | ||
auto_batch_size : bool or int or AutoBatchSize, default: True | ||
If True, automatic batch size will be used. If int, it will be used | ||
as the initial batch size. | ||
neighbor_list : ase.neighborlist.NewPrimitiveNeighborList, optional | ||
The ASE neighbor list class to produce the neighbor list. If None, the | ||
neighbor list will be built natively in the model. | ||
**kwargs : dict | ||
Keyword arguments. | ||
""" | ||
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@property | ||
def output_def(self) -> ModelOutputDef: | ||
"""Get the output definition of this model.""" | ||
return ModelOutputDef( | ||
FittingOutputDef( | ||
[ | ||
OutputVariableDef( | ||
"dos", | ||
shape=[-1], | ||
reduciable=True, | ||
atomic=True, | ||
), | ||
] | ||
) | ||
) | ||
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def eval( | ||
self, | ||
coords: np.ndarray, | ||
cells: Optional[np.ndarray], | ||
atom_types: Union[List[int], np.ndarray], | ||
atomic: bool = False, | ||
fparam: Optional[np.ndarray] = None, | ||
aparam: Optional[np.ndarray] = None, | ||
mixed_type: bool = False, | ||
**kwargs: Dict[str, Any], | ||
) -> Tuple[np.ndarray, ...]: | ||
"""Evaluate energy, force, and virial. If atomic is True, | ||
also return atomic energy and atomic virial. | ||
Parameters | ||
---------- | ||
coords : np.ndarray | ||
The coordinates of the atoms, in shape (nframes, natoms, 3). | ||
cells : np.ndarray | ||
The cell vectors of the system, in shape (nframes, 9). If the system | ||
is not periodic, set it to None. | ||
atom_types : List[int] or np.ndarray | ||
The types of the atoms. If mixed_type is False, the shape is (natoms,); | ||
otherwise, the shape is (nframes, natoms). | ||
atomic : bool, optional | ||
Whether to return atomic energy and atomic virial, by default False. | ||
fparam : np.ndarray, optional | ||
The frame parameters, by default None. | ||
aparam : np.ndarray, optional | ||
The atomic parameters, by default None. | ||
mixed_type : bool, optional | ||
Whether the atom_types is mixed type, by default False. | ||
**kwargs : Dict[str, Any] | ||
Keyword arguments. | ||
Returns | ||
------- | ||
energy | ||
The energy of the system, in shape (nframes,). | ||
force | ||
The force of the system, in shape (nframes, natoms, 3). | ||
virial | ||
The virial of the system, in shape (nframes, 9). | ||
atomic_energy | ||
The atomic energy of the system, in shape (nframes, natoms). Only returned | ||
when atomic is True. | ||
atomic_virial | ||
The atomic virial of the system, in shape (nframes, natoms, 9). Only returned | ||
when atomic is True. | ||
""" | ||
( | ||
coords, | ||
cells, | ||
atom_types, | ||
fparam, | ||
aparam, | ||
nframes, | ||
natoms, | ||
) = self._standard_input(coords, cells, atom_types, fparam, aparam, mixed_type) | ||
results = self.deep_eval.eval( | ||
coords, | ||
cells, | ||
atom_types, | ||
atomic, | ||
fparam=fparam, | ||
aparam=aparam, | ||
**kwargs, | ||
) | ||
# energy = results["dos_redu"].reshape(nframes, self.get_numb_dos()) | ||
atomic_energy = results["dos"].reshape(nframes, natoms, self.get_numb_dos()) | ||
# not same as dos_redu... why? | ||
energy = np.sum(atomic_energy, axis=1) | ||
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if atomic: | ||
return ( | ||
energy, | ||
atomic_energy, | ||
) | ||
else: | ||
return (energy,) | ||
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def get_numb_dos(self) -> int: | ||
return self.deep_eval.get_numb_dos() | ||
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__all__ = ["DeepDOS"] |
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