deepmd.dpmodel.fitting.dos_fitting

Module Contents

Classes

DOSFittingNet

Fitting the energy (or a rotationally invariant porperty of dim_out) of the system. The force and the virial can also be trained.

class deepmd.dpmodel.fitting.dos_fitting.DOSFittingNet(ntypes: int, dim_descrpt: int, numb_dos: int = 300, neuron: List[int] = [120, 120, 120], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, bias_dos: numpy.ndarray | None = None, rcond: float | None = None, trainable: bool | List[bool] = True, activation_function: str = 'tanh', precision: str = DEFAULT_PRECISION, mixed_types: bool = False, exclude_types: List[int] = [], seed: int | None = None)

Bases: deepmd.dpmodel.fitting.invar_fitting.InvarFitting

Fitting the energy (or a rotationally invariant porperty of dim_out) of the system. The force and the virial can also be trained.

Lets take the energy fitting task as an example. The potential energy \(E\) is a fitting network function of the descriptor \(\mathcal{D}\):

\[E(\mathcal{D}) = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)} \circ \cdots \circ \mathcal{L}^{(1)} \circ \mathcal{L}^{(0)}\]

The first \(n\) hidden layers \(\mathcal{L}^{(0)}, \cdots, \mathcal{L}^{(n-1)}\) are given by

\[\mathbf{y}=\mathcal{L}(\mathbf{x};\mathbf{w},\mathbf{b})= \boldsymbol{\phi}(\mathbf{x}^T\mathbf{w}+\mathbf{b})\]

where \(\mathbf{x} \in \mathbb{R}^{N_1}\) is the input vector and \(\mathbf{y} \in \mathbb{R}^{N_2}\) is the output vector. \(\mathbf{w} \in \mathbb{R}^{N_1 \times N_2}\) and \(\mathbf{b} \in \mathbb{R}^{N_2}\) are weights and biases, respectively, both of which are trainable if trainable[i] is True. \(\boldsymbol{\phi}\) is the activation function.

The output layer \(\mathcal{L}^{(n)}\) is given by

\[\mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})= \mathbf{x}^T\mathbf{w}+\mathbf{b}\]

where \(\mathbf{x} \in \mathbb{R}^{N_{n-1}}\) is the input vector and \(\mathbf{y} \in \mathbb{R}\) is the output scalar. \(\mathbf{w} \in \mathbb{R}^{N_{n-1}}\) and \(\mathbf{b} \in \mathbb{R}\) are weights and bias, respectively, both of which are trainable if trainable[n] is True.

Parameters:

var_name

The name of the output variable.

ntypes

The number of atom types.

dim_descrpt

The dimension of the input descriptor.

dim_out

The dimension of the output fit property.

neuron

Number of neurons \(N\) in each hidden layer of the fitting net

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

rcond

The condition number for the regression of atomic energy.

bias_atom

Bias for each element.

tot_ener_zero

Force the total energy to zero. Useful for the charge fitting.

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.

atom_ener

Specifying atomic energy contribution in vacuum. The set_davg_zero key in the descrptor should be set.

activation_function

The activation function \(\boldsymbol{\phi}\) in the embedding net. Supported options are “relu”, “tanh”, “none”, “linear”, “softplus”, “sigmoid”, “relu6”, “gelu”, “gelu_tf”.

precision

The precision of the embedding net parameters. Supported options are “float32”, “default”, “float16”, “float64”.

layer_namelist[Optional[str]], optional

The name of the each layer. If two layers, either in the same fitting or different fittings, have the same name, they will share the same neural network parameters.

use_aparam_as_mask: bool, optional

If True, the atomic parameters will be used as a mask that determines the atom is real/virtual. And the aparam will not be used as the atomic parameters for embedding.

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.

classmethod deserialize(data: dict) → deepmd.dpmodel.fitting.general_fitting.GeneralFitting

Deserialize the fitting.

Parameters:

datadict

The serialized data

Returns:

BF

The deserialized fitting

serialize() → dict

Serialize the fitting to dict.