ml-mlip-automl

What problem does it solve?

It helps you avoid slow, expensive trial-and-error when training machine learning interatomic potentials by automatically searching for effective hyperparameters.

Core Features & Use Cases

• LLM-driven iterative hyperparameter search: Uses validation MAE trends to decide the next parameter set to try instead of exhaustive grid search.
• Cross-framework fine-tuning orchestration: Coordinates foundation fine-tuning runs for MACE, MatGL, and FairChem to evaluate configurations consistently.
• Practical early stopping search strategy: Runs short trials (about ~10 epochs on small datasets) and stops after performance plateaus or repeated degradation.

Quick Start

Ask your agent to run a ~10-trial LLM-guided hyperparameter search for your MLIP dataset, read each run’s training_history.json to choose the next configuration, and then retrain the best setup for many epochs with early stopping.