mattertune.configs.backbones.orb

class mattertune.configs.backbones.orb.FinetuneModuleBaseConfig(*, properties, optimizer, lr_scheduler=None, ignore_gpu_batch_transform_error=True, normalizers={})

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()

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.

abstract create_model()

Creates an instance of the finetune module for this configuration.

Return type:

FinetuneModuleBase

class mattertune.configs.backbones.orb.ORBBackboneConfig(*, properties, optimizer, lr_scheduler=None, ignore_gpu_batch_transform_error=True, normalizers={}, name='orb', pretrained_model, system=ORBSystemConfig(radius=10.0, max_num_neighbors=20))

Parameters:

• properties (Sequence[PropertyConfig])

• optimizer (OptimizerConfig)

• lr_scheduler (LRSchedulerConfig | None)

• ignore_gpu_batch_transform_error (bool)

• normalizers (Mapping[str, Sequence[NormalizerConfig]])

• name (Literal['orb'])

• pretrained_model (str)

• system (ORBSystemConfig)

name: Literal['orb']

The type of the backbone.

pretrained_model: str

The name of the pretrained model to load.

system: ORBSystemConfig

The system configuration, controlling how to featurize a system of atoms.

create_model()

Creates an instance of the finetune module for this configuration.

classmethod ensure_dependencies()

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.orb.ORBSystemConfig(*, radius, max_num_neighbors)

Config controlling how to featurize a system of atoms.

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.

Modules

model