mattertune.configs.backbones.eqV2
- class mattertune.configs.backbones.eqV2.EqV2BackboneConfig(*, properties, optimizer, lr_scheduler=None, ignore_gpu_batch_transform_error=True, normalizers={}, name='eqV2', checkpoint_path, atoms_to_graph)[source]
- Parameters:
properties (Sequence[PropertyConfig])
optimizer (OptimizerConfig)
lr_scheduler (LRSchedulerConfig | None)
ignore_gpu_batch_transform_error (bool)
normalizers (Mapping[str, Sequence[NormalizerConfig]])
name (Literal['eqV2'])
checkpoint_path (Path | CachedPath)
atoms_to_graph (FAIRChemAtomsToGraphSystemConfig)
- name: Literal['eqV2']
The type of the backbone.
- checkpoint_path: Path | CE.CachedPath
The path to the checkpoint to load.
- atoms_to_graph: FAIRChemAtomsToGraphSystemConfig
Configuration for converting ASE Atoms to a graph.
- classmethod ensure_dependencies()[source]
Ensure that all dependencies are installed.
This method should raise an exception if any dependencies are missing, with a message indicating which dependencies are missing and how to install them.
- properties: Sequence[PropertyConfig]
Properties to predict.
- optimizer: OptimizerConfig
Optimizer.
- lr_scheduler: LRSchedulerConfig | None
Learning Rate Scheduler
- ignore_gpu_batch_transform_error: bool
Whether to ignore data processing errors during training.
- normalizers: Mapping[str, Sequence[NormalizerConfig]]
Normalizers for the properties.
Any property can be associated with multiple normalizers. This is useful for cases where we want to normalize the same property in different ways. For example, we may want to normalize the energy by subtracting the atomic reference energies, as well as by mean and standard deviation normalization.
The normalizers are applied in the order they are defined in the list.
- class mattertune.configs.backbones.eqV2.FAIRChemAtomsToGraphSystemConfig(*, radius, max_num_neighbors)[source]
Configuration for converting ASE Atoms to a graph for the FAIRChem model.
- Parameters:
radius (float)
max_num_neighbors (int)
- radius: float
The radius for edge construction.
- max_num_neighbors: int
The maximum number of neighbours each node can send messages to.
- class mattertune.configs.backbones.eqV2.FinetuneModuleBaseConfig(*, properties, optimizer, lr_scheduler=None, ignore_gpu_batch_transform_error=True, normalizers={})[source]
- Parameters:
properties (Sequence[PropertyConfig])
optimizer (OptimizerConfig)
lr_scheduler (LRSchedulerConfig | None)
ignore_gpu_batch_transform_error (bool)
normalizers (Mapping[str, Sequence[NormalizerConfig]])
- properties: Sequence[PropertyConfig]
Properties to predict.
- optimizer: OptimizerConfig
Optimizer.
- lr_scheduler: LRSchedulerConfig | None
Learning Rate Scheduler
- ignore_gpu_batch_transform_error: bool
Whether to ignore data processing errors during training.
- normalizers: Mapping[str, Sequence[NormalizerConfig]]
Normalizers for the properties.
Any property can be associated with multiple normalizers. This is useful for cases where we want to normalize the same property in different ways. For example, we may want to normalize the energy by subtracting the atomic reference energies, as well as by mean and standard deviation normalization.
The normalizers are applied in the order they are defined in the list.
- abstract classmethod ensure_dependencies()[source]
Ensure that all dependencies are installed.
This method should raise an exception if any dependencies are missing, with a message indicating which dependencies are missing and how to install them.
Modules