deepmd.dpmodel.fitting.invar_fitting#
Classes#
Fitting the energy (or a rotationally invariant property of dim_out) of the system. The force and the virial can also be trained. |
Module Contents#
- class deepmd.dpmodel.fitting.invar_fitting.InvarFitting(var_name: str, ntypes: int, dim_descrpt: int, dim_out: int, neuron: list[int] = [120, 120, 120], resnet_dt: bool = True, numb_fparam: int = 0, numb_aparam: int = 0, dim_case_embd: int = 0, bias_atom: deepmd.dpmodel.array_api.Array | None = None, rcond: float | None = None, tot_ener_zero: bool = False, trainable: list[bool] | None = None, atom_ener: list[float] | None = None, activation_function: str = 'tanh', precision: str = DEFAULT_PRECISION, layer_name: list[str | None] | None = None, use_aparam_as_mask: bool = False, spin: Any = None, mixed_types: bool = True, exclude_types: list[int] = [], type_map: list[str] | None = None, seed: int | list[int] | None = None, default_fparam: list[float] | None = None)[source]#
Bases:
deepmd.dpmodel.fitting.general_fitting.GeneralFittingFitting the energy (or a rotationally invariant property 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 descriptor should be set.
- activation_function
The activation function \(\boldsymbol{\phi}\) in the embedding net. Supported options are “gelu”, “gelu_tf”, “relu”, “silut”, “none”, “silu”, “tanh”, “softplus”, “sigmoid”, “linear”, “relu6”.
- precision
The precision of the embedding net parameters. Supported options are “default”, “bfloat16”, “float64”, “float16”, “float32”.
- layer_name
list[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.
- type_map: list[str], Optional
A list of strings. Give the name to each type of atoms.
- default_fparam: list[float], optional
The default frame parameter. If set, when fparam.npy files are not included in the data system, this value will be used as the default value for the frame parameter in the fitting net.
- classmethod deserialize(data: dict) deepmd.dpmodel.fitting.general_fitting.GeneralFitting[source]#
Deserialize the fitting.
- Parameters:
- data
dict The serialized data
- data
- Returns:
BFThe deserialized fitting
- abstractmethod compute_output_stats(merged: Any) NoReturn[source]#
Update the output bias for fitting net.
- output_def() deepmd.dpmodel.output_def.FittingOutputDef[source]#
Returns the output def of the fitting net.
- call(descriptor: deepmd.dpmodel.array_api.Array, atype: deepmd.dpmodel.array_api.Array, gr: deepmd.dpmodel.array_api.Array | None = None, g2: deepmd.dpmodel.array_api.Array | None = None, h2: deepmd.dpmodel.array_api.Array | None = None, fparam: deepmd.dpmodel.array_api.Array | None = None, aparam: deepmd.dpmodel.array_api.Array | None = None) dict[str, deepmd.dpmodel.array_api.Array][source]#
Calculate the fitting.
- Parameters:
- descriptor
input descriptor. shape: nf x nloc x nd
- atype
the atom type. shape: nf x nloc
- gr
The rotationally equivariant and permutationally invariant single particle representation. shape: nf x nloc x ng x 3
- g2
The rotationally invariant pair-partical representation. shape: nf x nloc x nnei x ng
- h2
The rotationally equivariant pair-partical representation. shape: nf x nloc x nnei x 3
- fparam
The frame parameter. shape: nf x nfp. nfp being numb_fparam
- aparam
The atomic parameter. shape: nf x nloc x nap. nap being numb_aparam