deepmd.pt.loss.ener

Classes

EnergyStdLoss

Helper class that provides a standard way to create an ABC using

Module Contents

class deepmd.pt.loss.ener.EnergyStdLoss(starter_learning_rate=1.0, start_pref_e=0.0, limit_pref_e=0.0, start_pref_f=0.0, limit_pref_f=0.0, start_pref_v=0.0, limit_pref_v=0.0, start_pref_ae: float = 0.0, limit_pref_ae: float = 0.0, start_pref_pf: float = 0.0, limit_pref_pf: float = 0.0, relative_f: float | None = None, enable_atom_ener_coeff: bool = False, start_pref_gf: float = 0.0, limit_pref_gf: float = 0.0, numb_generalized_coord: int = 0, use_l1_all: bool = False, inference=False, **kwargs)

Bases: deepmd.pt.loss.loss.TaskLoss

Helper class that provides a standard way to create an ABC using inheritance.

starter_learning_rate

has_e

has_f

has_v

has_ae

has_pf

has_gf

start_pref_e

limit_pref_e

start_pref_f

limit_pref_f

start_pref_v

limit_pref_v

start_pref_ae

limit_pref_ae

start_pref_pf

limit_pref_pf

start_pref_gf

limit_pref_gf

relative_f

enable_atom_ener_coeff

numb_generalized_coord

use_l1_all

inference

forward(input_dict, model, label, natoms, learning_rate, mae=False)

Return loss on energy and force.

Parameters:

input_dictdict[str, torch.Tensor]

Model inputs.

modeltorch.nn.Module

Model to be used to output the predictions.

labeldict[str, torch.Tensor]

Labels.

natomsint

The local atom number.

Returns:

model_pred: dict[str, torch.Tensor]

Model predictions.

loss: torch.Tensor

Loss for model to minimize.

more_loss: dict[str, torch.Tensor]

Other losses for display.

property label_requirement: list[deepmd.utils.data.DataRequirementItem]

Return data label requirements needed for this loss calculation.