deepmd.dpmodel.fitting.dos_fitting

deepmd.dpmodel.fitting.dos_fitting#

Classes#

DOSFittingNet

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.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, dim_case_embd: int = 0, bias_dos: deepmd.dpmodel.array_api.Array | 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] = [], vacuum_ref: bool = False, type_map: list[str] | None = None, seed: int | list[int] | None = None, default_fparam: list | None = None)[source]#

Bases: deepmd.dpmodel.fitting.invar_fitting.InvarFitting

Fitting 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.
vacuum_ref: Reference the network output of every atom to the output of the same network for an isolated atom of the same type under the same conditioning, so that an atom without neighbors contributes exactly its output bias.
activation_function: The activation function \(\boldsymbol{\phi}\) in the embedding net. Supported options are “gelu_tf”, “silu”, “tanh”, “gelu_erf”, “sigmoid”, “silut”, “softplus”, “relu6”, “relu”, “linear”, “none”, “gelu”.
precision: The precision of the embedding net parameters. Supported options are “default”, “float64”, “float32”, “bfloat16”, “float16”.
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.
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.

__doc__ = Multiline-String[source]#

Show Value

"""Fit DOS vectors :math:`\mathbf D_i=F_\theta(\mathcal D_i)`.

        Fitting 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 :math:`E` is a fitting network function of the descriptor :math:`\mathcal{D}`:

    .. math::
        E(\mathcal{D}) = \mathcal{L}^{(n)} \circ \mathcal{L}^{(n-1)}
        \circ \cdots \circ \mathcal{L}^{(1)} \circ \mathcal{L}^{(0)}

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

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

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

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

    .. math::
        \mathbf{y}=\mathcal{L}^{(n)}(\mathbf{x};\mathbf{w},\mathbf{b})=
            \mathbf{x}^T\mathbf{w}+\mathbf{b}

    where :math:`\mathbf{x} \in \mathbb{R}^{N_{n-1}}` is the input vector and :math:`\mathbf{y} \in \mathbb{R}`
    is the output scalar. :math:`\mathbf{w} \in \mathbb{R}^{N_{n-1}}` and
    :math:`\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 :math:`N` in each hidden layer of the fitting net
    resnet_dt
            Time-step `dt` in the resnet construction:
            :math:`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 :math:`N_l` hidden layers in the fitting net,
            this list is of length :math:`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.
    vacuum_ref
            Reference the network output of every atom to the output of the same
            network for an isolated atom of the same type under the same
            conditioning, so that an atom without neighbors contributes exactly
            its output bias.
    activation_function
            The activation function :math:`\boldsymbol{\phi}` in the embedding net. Supported options are |ACTIVATION_FN|
    precision
            The precision of the embedding net parameters. Supported options are |PRECISION|
    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.

    """

output_def() → deepmd.dpmodel.output_def.FittingOutputDef[source]#: Returns the output def of the fitting net.

classmethod deserialize(data: dict) → deepmd.dpmodel.fitting.general_fitting.GeneralFitting[source]#

Deserialize the fitting.

Parameters:

datadict: The serialized data

Returns:

BF: The deserialized fitting

serialize() → dict[source]#: Serialize the fitting to dict.