`deepmd.tf.infer.deep_polar`

Module Contents

`DeepGlobalPolar`	Deep Tensor Model.
`DeepPolar`	Deep polar model.

class deepmd.tf.infer.deep_polar.DeepGlobalPolar(model_file: str, *args: List[Any], auto_batch_size: bool | int | deepmd.utils.batch_size.AutoBatchSize = True, neighbor_list: ase.neighborlist.NewPrimitiveNeighborList | None = None, **kwargs: Dict[str, Any])[source]

Deep Tensor Model.

Parameters:

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool 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_listase.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.
**kwargsdict: Keyword arguments.

eval(coords: numpy.ndarray, cells: numpy.ndarray | None, atom_types: List[int] | numpy.ndarray, atomic: bool = False, fparam: numpy.ndarray | None = None, aparam: numpy.ndarray | None = None, mixed_type: bool = False, **kwargs: dict) → numpy.ndarray[source]

Evaluate the model.

Parameters:

coords: The coordinates of atoms. The array should be of size nframes x natoms x 3
cells: The cell of the region. If None then non-PBC is assumed, otherwise using PBC. The array should be of size nframes x 9
atom_typeslist[int] or np.ndarray: The atom types The list should contain natoms ints
atomic: If True (default), return the atomic tensor Otherwise return the global tensor
fparam: Not used in this model
aparam: Not used in this model
mixed_type: Whether to perform the mixed_type mode. If True, the input data has the mixed_type format (see doc/model/train_se_atten.md), in which frames in a system may have different natoms_vec(s), with the same nloc.

Returns:

tensor: The returned tensor If atomic == False then of size nframes x output_dim else of size nframes x natoms x output_dim

class deepmd.tf.infer.deep_polar.DeepPolar(model_file: str, *args: List[Any], auto_batch_size: bool | int | deepmd.utils.batch_size.AutoBatchSize = True, neighbor_list: ase.neighborlist.NewPrimitiveNeighborList | None = None, **kwargs: Dict[str, Any])[source]

Deep polar model.

Parameters:

model_filePath: The name of the frozen model file.
*argslist: Positional arguments.
auto_batch_sizebool 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_listase.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.
**kwargsdict: Keyword arguments.