deepmd.pt.model.task.property

deepmd.pt.model.task.property#

Attributes#

dtype

device

log

Classes#

PropertyFittingNet

Fitting the rotationally invariant properties of task_dim of the system.

Module Contents#

deepmd.pt.model.task.property.dtype[source]#
deepmd.pt.model.task.property.device[source]#
deepmd.pt.model.task.property.log[source]#
class deepmd.pt.model.task.property.PropertyFittingNet(ntypes: int, dim_descrpt: int, task_dim: int = 1, neuron: list[int] = [128, 128, 128], bias_atom_p: torch.Tensor | None = None, intensive: bool = False, bias_method: str = 'normal', resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, activation_function: str = 'tanh', precision: str = DEFAULT_PRECISION, mixed_types: bool = True, seed: int | None = None, **kwargs)[source]#

Bases: deepmd.pt.model.task.ener.InvarFitting

Fitting the rotationally invariant properties of task_dim of the system.

Parameters:
ntypesint

Element count.

dim_descrptint

Embedding width per atom.

task_dimint

The dimension of outputs of fitting net.

neuronlist[int]

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

bias_atom_ptorch.Tensor, optional

Average property per atom for each element.

intensivebool, optional

Whether the fitting property is intensive.

bias_methodstr, optional

The method of applying the bias to each atomic output, user can select ‘normal’ or ‘no_bias’. If ‘normal’ is used, the computed bias will be added to the atomic output. If ‘no_bias’ is used, no bias will be added to the atomic output.

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.

seedint, optional

Random seed.

task_dim[source]#
intensive[source]#
bias_method[source]#
get_bias_method() → str[source]#
output_def() → deepmd.dpmodel.FittingOutputDef[source]#

Returns the output def of the fitting net.

classmethod deserialize(data: dict) → PropertyFittingNet[source]#

Deserialize the fitting.

Parameters:
datadict

The serialized data

Returns:
BF

The deserialized fitting

serialize() → dict[source]#

Serialize the fitting to dict.

exclude_types: list[int][source]#