deepmd.pt.loss.ener_spin

Classes

EnergySpinLoss

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

Module Contents

class deepmd.pt.loss.ener_spin.EnergySpinLoss(starter_learning_rate=1.0, start_pref_e=0.0, limit_pref_e=0.0, start_pref_fr=0.0, limit_pref_fr=0.0, start_pref_fm=0.0, limit_pref_fm=0.0, start_pref_v=0.0, limit_pref_v=0.0, start_pref_ae: float = 0.0, limit_pref_ae: float = 0.0, enable_atom_ener_coeff: bool = False, 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_fr

has_fm

has_v

has_ae

start_pref_e

limit_pref_e

start_pref_fr

limit_pref_fr

start_pref_fm

limit_pref_fm

start_pref_v

limit_pref_v

start_pref_ae

limit_pref_ae

enable_atom_ener_coeff

use_l1_all

inference

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

Return energy loss with magnetic labels.

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.