- class light_pfp_autogen.active_learning.ActiveLearning(active_learning_config: ActiveLearningConfig)#
-
Bases:
object
Interface for active learning in molecular dynamics (MD) simulations.
- clean_up_models(keep_final: bool = True) None #
- initialize() None #
-
Initialize the active learning process.
Steps:
1. Perform initial training if not already done.
2. Call _check_previous_iterations method to check the status of previous iterations,and clean up possible conflicted files.
-
Call _pre_sampling method to prepare for the first MD sampling.
-
- print_md_statistics(start: int = 0, stop: Optional[int] = None) None #
-
Prints the statistics of the MD sampling.
- Parameters
-
-
start (int) – The starting iteration number. Default is 0.
-
stop (int) – The stopping iteration number. If None, it will be set to
the current iteration. Default is None.
-
- print_training_statistics(start: int = -1, stop: Optional[int] = None) None #
-
Prints the energy forces and stress MAE of the models.
- Parameters
-
-
start (int) – The starting iteration number. If -1, it will be set to the initial model.
Default is -1. -
stop (int) – The stopping iteration number. If None, it will be set to
the current iteration. Default is None.
-
- update() None #
-
Train the new model with the collected data and prepare for the next MD sampling.
Also, the iteration count is incremented by 1.
Resource Library
Active Learning