deepmd.pt.model.task.polarizability

Attributes

log

Classes

PolarFittingNet

Construct a polar fitting net.

Module Contents

deepmd.pt.model.task.polarizability.log

class deepmd.pt.model.task.polarizability.PolarFittingNet(ntypes: int, dim_descrpt: int, embedding_width: int, neuron: list[int] = [128, 128, 128], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, activation_function: str = 'tanh', precision: str = DEFAULT_PRECISION, mixed_types: bool = True, rcond: float | None = None, seed: int | list[int] | None = None, exclude_types: list[int] = [], fit_diag: bool = True, scale: list[float] | float | None = None, shift_diag: bool = True, type_map: list[str] | None = None, **kwargs)

Bases: deepmd.pt.model.task.fitting.GeneralFitting

Construct a polar fitting net.

Parameters:

ntypesint

Element count.

dim_descrptint

Embedding width per atom.

embedding_widthint

The dimension of rotation matrix, m1.

neuronlist[int]

Number of neurons in each hidden layers of the fitting net.

resnet_dtbool

Using time-step in the ResNet construction.

numb_fparamint

Number of frame parameters.

numb_aparamint

Number of atomic parameters.

activation_functionstr

Activation function.

precisionstr

Numerical precision.

mixed_typesbool

If true, use a uniform fitting net for all atom types, otherwise use different fitting nets for different atom types.

rcondfloat, optional

The condition number for the regression of atomic energy.

seedint, optional

Random seed.

fit_diagbool

Fit the diagonal part of the rotational invariant polarizability matrix, which will be converted to normal polarizability matrix by contracting with the rotation matrix.

scalelist[float]

The output of the fitting net (polarizability matrix) for type i atom will be scaled by scale[i]

shift_diagbool

Whether to shift the diagonal part of the polarizability matrix. The shift operation is carried out after scale.

type_map: list[str], Optional

A list of strings. Give the name to each type of atoms.

embedding_width

fit_diag

scale

shift_diag

constant_matrix

_net_out_dim()

Set the FittingNet output dim.

__setitem__(key, value) → None

__getitem__(key)

change_type_map(type_map: list[str], model_with_new_type_stat=None) → None

Change the type related params to new ones, according to type_map and the original one in the model. If there are new types in type_map, statistics will be updated accordingly to model_with_new_type_stat for these new types.

serialize() → dict

Serialize the fitting to dict.

classmethod deserialize(data: dict) → deepmd.pt.model.task.fitting.GeneralFitting

Deserialize the fitting.

Parameters:

datadict

The serialized data

Returns:

BF

The deserialized fitting

output_def() → deepmd.dpmodel.FittingOutputDef

Returns the output def of the fitting net.

forward(descriptor: torch.Tensor, atype: torch.Tensor, gr: torch.Tensor | None = None, g2: torch.Tensor | None = None, h2: torch.Tensor | None = None, fparam: torch.Tensor | None = None, aparam: torch.Tensor | None = None)

exclude_types: list[int]