Loss¶

class mlip.models.loss.Loss¶

Very simple loss base class that defines the signature of the call function.

abstract __call__(prediction: Prediction, ref_graph: GraphsTuple, epoch: int, eval_metrics: bool = False) → tuple[float, dict[str, float]]¶

The call function that outputs the loss and metrics (auxiliary data).

Parameters:

prediction – The force field predictor’s outputs.
ref_graph – The reference graph holding the ground truth data.
epoch – The epoch number.
eval_metrics – Switch deciding whether to include additional evaluation metrics to the returned dictionary. Default is False.

Returns:

The loss and the auxiliary metrics dictionary.

class mlip.models.loss.WeightedEFSLoss(energy_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, forces_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, stress_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, extended_metrics: bool = False)¶

Loss base class for scheduled average of energy, forces and stress errors.

__init__(energy_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, forces_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, stress_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, extended_metrics: bool = False) → None¶

Loss averaging energy, forces and stress errors with epoch-dependent weights.

Parameters:

energy_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 1.
forces_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 25.
stress_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 0.
extended_metrics – Whether to include an extended list of metrics. Defaults to False.

__call__(prediction: Prediction, ref_graph: GraphsTuple, epoch: int, eval_metrics: bool = False) → tuple[float, dict[str, float]]¶

The call function that outputs the loss and metrics (auxiliary data).

Parameters:

prediction – The force field predictor’s outputs.
ref_graph – The reference graph holding the ground truth data.
epoch – The epoch number.
eval_metrics – Switch deciding whether to include additional evaluation metrics to the returned dictionary. Default is False.

Returns:

The loss and the auxiliary metrics dictionary.

class mlip.models.loss.MSELoss(energy_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, forces_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, stress_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, extended_metrics: bool = False)¶

Mean squared-error loss for scheduled average of energy, forces and stress errors.

Loss averaging energy, forces and stress errors with epoch-dependent weights.

Parameters:

energy_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 1.
forces_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 25.
stress_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 0.
extended_metrics – Whether to include an extended list of metrics. Defaults to False.

__call__(prediction: Prediction, ref_graph: GraphsTuple, epoch: int, eval_metrics: bool = False) → tuple[float, dict[str, float]]¶

The call function that outputs the loss and metrics (auxiliary data).

Parameters:

prediction – The force field predictor’s outputs.
ref_graph – The reference graph holding the ground truth data.
epoch – The epoch number.
eval_metrics – Switch deciding whether to include additional evaluation metrics to the returned dictionary. Default is False.

Returns:

The loss and the auxiliary metrics dictionary.

class mlip.models.loss.HuberLoss(energy_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, forces_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, stress_weight_schedule: ~typing.Callable[[int], float] = <function WeightedEFSLoss.<lambda>>, extended_metrics: bool = False)¶

Huber loss for scheduled average of energy, forces and stress errors.

Loss averaging energy, forces and stress errors with epoch-dependent weights.

Parameters:

energy_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 1.
forces_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 25.
stress_weight_schedule – The schedule function for the energy weight. Default is a constant weight of 0.
extended_metrics – Whether to include an extended list of metrics. Defaults to False.

__call__(prediction: Prediction, ref_graph: GraphsTuple, epoch: int, eval_metrics: bool = False) → tuple[float, dict[str, float]]¶

The call function that outputs the loss and metrics (auxiliary data).

Parameters:

prediction – The force field predictor’s outputs.
ref_graph – The reference graph holding the ground truth data.
epoch – The epoch number.
eval_metrics – Switch deciding whether to include additional evaluation metrics to the returned dictionary. Default is False.

Returns:

The loss and the auxiliary metrics dictionary.