Config

Bases: BaseModel

The configuration for active learning

task_name#

The name of the task. The model name will be {task_name}_N
where N is the iteration index. Default is “active_learning”.

Type

str

init_dataset#

The initial dataset for the first iteration.

Type

Sequence[Union[Path, str]]

pfp_model_version#

The version of the PFP model for data augmentation.

Type

str

pfp_calc_mode#

The calculation mode of the PFP model for data augmentation.

Type

str

train_config#

The configuration for training. Default is TrainConfig().

Type

TrainConfig

training_time#

The time required for model updates during each active learning iteration
in hours. The total number of epochs for model training is adjusted according
to the size of the current dataset while keeping the training time roughly
around the training_time hours. Defaults to 0.5.

Type

float

work_dir#

The working directory to store training data, etc, during the iterations.
Defaults to ./autogen_workdir.

Type

Union[str, Path]

sample_config#

The configuration for selecting new training data. Default is SampleConfig().

Type

SampleConfig

classmethod from_dict(config_dict: Dict) → ActiveLearningConfig#

classmethod from_json(config_file: str) → ActiveLearningConfig#

init_dataset: Sequence[Union[Path, str]]#

pfp_calc_mode: str#

pfp_model_version: str#

sample_config: SampleConfig#

task_name: str#

to_json(config_file: str) → None#

train_config: TrainConfig#

training_time: float#

classmethod validate_init_dataset(value: Sequence[Path]) → Sequence[Path]#

classmethod validate_training_time(value: float) → float#

work_dir: Union[str, Path]#

Bases: ActiveLearningConfig

The configuration for active learning CLI

md_script#

The path to the MD script.

Type

str

num_iteration#

The number of iterations for active learning. Default is 10.

Type

int

classmethod check_md_script(value: str) → str#

classmethod check_num_iteration(value: int) → int#

classmethod from_dict(config_dict: Dict) → CLIConfig#

classmethod from_json(config_file: str) → CLIConfig#

md_script: str#

num_iteration: int#

Bases: BaseModel

The common configuration for training

total_epoch#

Number of epochs the entire multistage training process will run for.
Divided in a 0.25:0.5:0.25 ratio between stage1/stage2/calibration stages.
Default is 0.

Type

int

batch_size#

Number of training structures used for one time of parameter update.
Default is 128.

Type

int

reload_model#

If a model ID is provided, the training job will be started from the given model.
If empty, the parameters of the model will be randomly initialized.
Default is “”.

Type

str

lr_scheduling#

The learning rate scheduler. Support “step”, “linear_warmup” and “None”.
Default is “linear_warmup”.

Type

str

max_energy#

The max energy per atom allowed in the training dataset. The structure will be
skipped if the energy is larger than this value. Default is 1e38.

Type

float

max_forces#

The max forces allowed in the training dataset. The structure will be skipped if
the force acting on one atom is larger than this value. Default is 1e38.

Type

float

batch_size: int#

lr_scheduling: str#

max_energy: float#

max_forces: float#

reload_model: str#

total_epoch: int#

Bases: BaseModel

The configuration for MTP model

model_version#

The version of the MTP model. Default is “v1.0”.

Type

str

pretrained_model#

Use a pre-trained model. If empty, the pre-trained model is not used. Default is “”.

Type

str

rc#

The cutoff distance to define the local environment of one atom. Default is 6.0.

Type

float

levmax#

The argument to control the complexity of the moment descriptor. Default is 8.

Type

int

moment_init_cost#

The argument to control the complexity of the moment descriptor. Default is 2.

Type

int

moment_mu_cost#

The argument to control the complexity of the moment descriptor. Default is 1.

Type

int

moment_nu_cost#

The argument to control the complexity of the moment descriptor. Default is 1.

Type

int

levmax: int#

model_version: str#

moment_init_cost: int#

moment_mu_cost: int#

moment_nu_cost: int#

pretrained_model: str#

rc: float#

Bases: BaseModel

The criteria for selecting new training data in active learning

dE_min_coef#

Minimum threshold for energy error, as a multiple of MAE.

Type

float, Optional

dE_min#

Minimum threshold for energy error, absolute value (eV/atom).

Type

float, Optional

dE_max_coef#

Maximum threshold for energy error, as a multiple of MAE.

Type

float, Optional

dE_max#

Maximum threshold for energy error, absolute value (eV/atom).

Type

float, Optional

dF_min_coef#

Minimum threshold for forces error, as a multiple of MAE.

Type

float, Optional

dF_min#

Minimum threshold for forces error, absolute value (eV/Å).

Type

float, Optional

dF_max_coef#

Maximum threshold for forces error, as a multiple of MAE.

Type

float, Optional

dF_max#

Maximum threshold for forces error, absolute value (eV/Å).

Type

float, Optional

dS_min_coef#

Minimum threshold for stress error, as a multiple of MAE.

Type

float, Optional

dS_min#

Minimum threshold for stress error, absolute value (GPa).

Type

float, Optional

dS_max_coef#

Maximum threshold for stress error, as a multiple of MAE.

Type

float, Optional

dS_max#

Maximum threshold for stress error, absolute value (GPa).

Type

float, Optional

pfp_fallback_samples#

Number of additional training structures to collect after switching to PFP calculator.
If 0, the PFP-based fallback mechanism is disabled. Defaults to 5.

Type

int, Optional

dE_max: Optional[float]#

dE_max_coef: Optional[float]#

dE_min: Optional[float]#

dE_min_coef: Optional[float]#

dF_max: Optional[float]#

dF_max_coef: Optional[float]#

dF_min: Optional[float]#

dF_min_coef: Optional[float]#

dS_max: Optional[float]#

dS_max_coef: Optional[float]#

dS_min: Optional[float]#

dS_min_coef: Optional[float]#

get_sample_range(energy_mae: float, forces_mae: float, stress_mae: float) → SampleRange#

Get the sample range based on the MAE of energy/forces/stress

pfp_fallback_samples: int#

validate_at_least_one_threshold() → SampleConfig#

classmethod validate_pfp_fallback_samples(value: int) → int#

Bases: BaseModel

The configuration for training

common_config#

The common configuration for training.

Type

CommonConfig

mtp_config#

The configuration for MTP model.

Type

MTPConfig

common_config: CommonConfig#

classmethod from_dict(config_dict: Dict) → TrainConfig#

Get the configuration from a dictionary
:param config_dict: The configuration dictionary
:type config_dict: Dict

Returns

The configuration object

Return type

TrainConfig

classmethod from_json(config_file: str) → TrainConfig#

Get the configuration from a json file
:param config_file: The path to the json file
:type config_file: str

Returns

The configuration object

Return type

TrainConfig

mtp_config: MTPConfig#

to_dict() → Dict#

to_json(config_file: str) → None#