MACE Fine-tuning

Goal

To evaluate and improve the accuracy of a foundation MACE potential for a specific chemical system or physical property using the provided Python fine-tuning script and data-augmentation.

Instructions

1. Prepare Labeled Dataset: Obtain diverse structures with high-fidelity labels (energy, forces, stress). See the /benchmark-finetuning workflow for details.
2. Custom Data Conversion: Read the source data format and write a customized conversion script if needed, formatting it for the subsequent preparation step.
3. Benchmarking: Predict results on the new labels and benchmark the foundation model using ml-mlip-benchmark.
4. Data Preparation: Execute scripts/prepare_mace_data.py to convert JSON structures to .xyz data files.
5. Config Generation: Execute scripts/generate_mace_config.py using the .xyz data to produce finetune_config.yaml.
6. Fine-Tuning: Execute mace_run_train --config /path/to/finetune_config.yaml to begin fine-tuning natively on the GPU.
7. Validation: Verify convergence and compare against the benchmarked foundation metrics.
8. Registration: Use the register_model tool to register the newly fine-tuned model checkpoint into the local registry so future research tasks can discover and reuse it.

Training Configuration

MACE fine-tuning is divided into a data preparation step, a configuration generation step, and a standard native training run. The script scripts/prepare_mace_data.py generates .xyz files, and scripts/generate_mace_config.py converts arguments into a fully-formed finetune_config.yaml configuration compatible with the MACE default parser.