deepmd.pd.model.task.ener

Attributes

dtype
device
log

Classes

EnergyFittingNet

Construct a fitting net for energy.

Module Contents

deepmd.pd.model.task.ener.dtype

deepmd.pd.model.task.ener.device = 'cpu'

deepmd.pd.model.task.ener.log

class deepmd.pd.model.task.ener.EnergyFittingNet(ntypes: int, dim_descrpt: int, neuron: list[int] = [128, 128, 128], bias_atom_e: paddle.Tensor | None = None, 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, seed: int | list[int] | None = None, type_map: list[str] | None = None, **kwargs: Any)

Bases: deepmd.pd.model.task.invar_fitting.InvarFitting

Construct a fitting net for energy.

Parameters:

var_namestr

The atomic property to fit, ‘energy’, ‘dipole’, and ‘polar’.

ntypesint

Element count.

dim_descrptint

Embedding width per atom.

dim_outint

The output dimension of the fitting net.

neuronlist[int]

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

bias_atom_epaddle.Tensor, optional

Average energy per atom for each element.

resnet_dtbool

Using time-step in the ResNet construction.

numb_fparamint

Number of frame parameters.

numb_aparamint

Number of atomic parameters.

dim_case_embdint

(Not supported yet) 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.

exclude_types: list[int]

Atomic contributions of the excluded atom types are set zero.

atom_ener: list[Optional[paddle.Tensor]], optional

Specifying atomic energy contribution in vacuum. The value is a list specifying the bias. the elements can be None or np.array of output shape. For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.] The set_davg_zero key in the descriptor should be set.

type_map: list[str], Optional

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

use_aparam_as_mask: bool

If True, the aparam will not be used in fitting net for embedding.

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

Deserialize the fitting.

Parameters:

datadict

The serialized data

Returns:

BF

The deserialized fitting

serialize() → dict

Serialize the fitting to dict.

exclude_types: list[int]