deepmd.pt.model.task.dipole

Attributes

log

Classes

DipoleFittingNet

Construct a dipole fitting net.

Module Contents

deepmd.pt.model.task.dipole.log

class deepmd.pt.model.task.dipole.DipoleFittingNet(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, dim_case_embd: 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] = [], r_differentiable: bool = True, c_differentiable: bool = True, type_map: list[str] | None = None, **kwargs)

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

Construct a dipole 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.

dim_case_embdint

Dimension of case specific embedding.

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.

r_differentiable

If the variable is differentiated with respect to coordinates of atoms. Only reducible variable are differentiable.

c_differentiable

If the variable is differentiated with respect to the cell tensor (pbc case). Only reducible variable are differentiable.

type_map: list[str], Optional

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

embedding_width

r_differentiable = True

c_differentiable = True

_net_out_dim()

Set the FittingNet output dim.

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.

compute_output_stats(merged: Callable[[], list[dict]] | list[dict], stat_file_path: deepmd.utils.path.DPPath | None = None) → None

Compute the output statistics (e.g. energy bias) for the fitting net from packed data.

Parameters:

mergedUnion[Callable[[], list[dict]], list[dict]]

• list[dict]: A list of data samples from various data systems.

  Each element, merged[i], is a data dictionary containing keys: torch.Tensor originating from the i-th data system.

• Callable[[], list[dict]]: A lazy function that returns data samples in the above format

  only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once.

stat_file_pathOptional[DPPath]

The path to the stat file.

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]