deepmd.pt.model.task.ener

deepmd.pt.model.task.ener#

Attributes#

`dtype`
`device`
`log`

Classes#

`EnergyFittingNet`	Construct a fitting net for energy.
`EnergyFittingNetDirect`	Base fitting provides the interfaces of fitting net.

Module Contents#

deepmd.pt.model.task.ener.dtype[source]#

deepmd.pt.model.task.ener.device[source]#

deepmd.pt.model.task.ener.log[source]#

class deepmd.pt.model.task.ener.EnergyFittingNet(ntypes: int, dim_descrpt: int, neuron: list[int] = [128, 128, 128], bias_atom_e: torch.Tensor | None = None, 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 | list[int] | None = None, type_map: list[str] | None = None, **kwargs)[source]#

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

Construct a fitting net for energy.

Parameters:

var_namestr: The atomic property to fit, ‘energy’, ‘dipole’, and ‘polar’.
ntypesint: Element count.
dim_descrptint: Embedding width per atom.
dim_outint: The output dimension of the fitting net.
neuronlist[int]: Number of neurons in each hidden layers of the fitting net.
bias_atom_etorch.Tensor, optional: Average energy per atom for each element.
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.
rcondfloat, optional: The condition number for the regression of atomic energy.
seedint, optional: Random seed.
exclude_types: list[int]: Atomic contributions of the excluded atom types are set zero.
atom_ener: list[Optional[torch.Tensor]], optional: Specifying atomic energy contribution in vacuum. The value is a list specifying the bias. the elements can be None or np.array of output shape. For example: [None, [2.]] means type 0 is not set, type 1 is set to [2.] The set_davg_zero key in the descriptor should be set.
type_map: list[str], Optional: A list of strings. Give the name to each type of atoms.
use_aparam_as_mask: bool: If True, the aparam will not be used in fitting net for embedding.

classmethod deserialize(data: dict) → deepmd.pt.model.task.fitting.GeneralFitting[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]#

class deepmd.pt.model.task.ener.EnergyFittingNetDirect(ntypes, dim_descrpt, neuron, bias_atom_e=None, out_dim=1, resnet_dt=True, use_tebd=True, return_energy=False, **kwargs)[source]#

Bases: deepmd.pt.model.task.fitting.Fitting

Base fitting provides the interfaces of fitting net.

ntypes[source]#

dim_descrpt[source]#

use_tebd = True[source]#

out_dim = 1[source]#

filter_layers_dipole[source]#

return_energy = False[source]#

filter_layers[source]#

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

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

abstractmethod deserialize() → EnergyFittingNetDirect[source]#

Deserialize the fitting.

Parameters:

datadict: The serialized data

Returns:

BF: The deserialized fitting

abstractmethod change_type_map(type_map: list[str], model_with_new_type_stat=None) → None[source]#: Change the type related params to new ones, according to type_map and the original one in the model. If there are new types in type_map, statistics will be updated accordingly to model_with_new_type_stat for these new types.

abstractmethod get_type_map() → list[str][source]#: Get the name to each type of atoms.

forward(inputs: torch.Tensor, atype: torch.Tensor, gr: torch.Tensor | None = None, g2: torch.Tensor | None = None, h2: torch.Tensor | None = None, fparam: torch.Tensor | None = None, aparam: torch.Tensor | None = None) → tuple[torch.Tensor, None][source]#

Based on embedding net output, alculate total energy.

Args: - inputs: Embedding matrix. Its shape is [nframes, natoms[0], self.dim_descrpt]. - natoms: Tell atom count and element count. Its shape is [2+self.ntypes].

Returns:

torch.Tensor: Total energy with shape [nframes, natoms[0]].