LLM-Coupled MLIP Hyperparameter Search (AutoML)

Goal

To automate the discovery of optimal training hyperparameters (learning rate, batch size, backbone freezing) by coupling the agent to an iterative execution loop. Instead of conducting exhaustive grid searches, the LLM actively interprets validation MAE trajectories per epoch and strategically deduces the next optimal parameter permutation to evaluate.

The Agentic Workflow

Instead of calling a monolithic training tool, this SKILL dictates how you (the Agent) should orchestrate a search using the individual foundation fine-tuning SKILLs (ml-mace-finetune, ml-matgl-finetune, ml-fairchem-finetune).

Execution Protocol

1. Initial Anchor: Select a conservative parameter set based on the dataset size (e.g., small dataset: freeze backbone, lower LR).
2. Execute Fine-Tuning: Create a dedicated subdirectory for the run (e.g., search_run_lr1e-3_frozen) and execute the respective MLIP's prepare_*_data.py and training runner.
3. Parse and Evaluate: Read the resulting training_history.json directly into your context. Analyze the validation loss and MAE curves (slope, signs of overfitting/forgetting).
4. Iterate: Deduce the next logical parameter adjustment based on the evaluation (e.g. "Validation MAE plateaued early. Action: Unfreeze backbone & lower LR to 1e-4").
5. Terminate: Halt and report the best configuration once a performance target is met or degradation occurs repeatedly.

Hyperparameter Search Spaces