deepmd.dpmodel.fitting.property_fitting

deepmd.dpmodel.fitting.property_fitting#

Classes#

PropertyFittingNet

Fitting the rotationally invariant properties of task_dim of the system.

Module Contents#

class deepmd.dpmodel.fitting.property_fitting.PropertyFittingNet(ntypes: int, dim_descrpt: int, task_dim: int = 1, neuron: list[int] = [128, 128, 128], bias_atom_p: numpy.ndarray | None = None, rcond: float | None = None, trainable: bool | list[bool] = True, intensive: bool = False, property_name: str = 'property', 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, exclude_types: list[int] = [], type_map: list[str] | None = None, seed: int | None = None)[source]#

Bases: deepmd.dpmodel.fitting.invar_fitting.InvarFitting

Fitting the rotationally invariant properties of task_dim of the system.

Parameters:

ntypes: The number of atom types.
dim_descrpt: The dimension of the input descriptor.
task_dim: The dimension of outputs of fitting net.
neuron: Number of neurons \(N\) in each hidden layer of the fitting net
bias_atom_p: Average property per atom for each element.
rcond: The condition number for the regression of atomic energy.
trainable: If the weights of fitting net are trainable. Suppose that we have \(N_l\) hidden layers in the fitting net, this list is of length \(N_l + 1\), specifying if the hidden layers and the output layer are trainable.
intensive: Whether the fitting property is intensive.
property_name:: The name of fitting property, which should be consistent with the property name in the dataset. If the data file is named humo.npy, this parameter should be “humo”.
resnet_dt: Time-step dt in the resnet construction: \(y = x + dt * \phi (Wx + b)\)
numb_fparam: Number of frame parameter
numb_aparam: Number of atomic parameter
activation_function: The activation function \(\boldsymbol{\phi}\) in the embedding net. Supported options are “gelu”, “tanh”, “gelu_tf”, “none”, “sigmoid”, “silu”, “silut”, “softplus”, “relu6”, “linear”, “relu”.
precision: The precision of the embedding net parameters. Supported options are “float16”, “float64”, “default”, “float32”.
mixed_types: If false, different atomic types uses different fitting net, otherwise different atom types share the same fitting net.
exclude_types: list[int]: Atomic contributions of the excluded atom types are set zero.
type_map: list[str], Optional: A list of strings. Give the name to each type of atoms.

task_dim = 1[source]#

intensive = False[source]#

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.