ml-mace-finetune

What problem does it solve?

MACE fine-tuning helps domain researchers adapt a foundation machine-learning interatomic potential to a specific chemical system or physical property when the out-of-the-box model is not accurate enough.

Core Features & Use Cases

• Convert labeled JSON into MACE-ready training data: prepares .xyz files with energy, forces, and optional stress labels (including VASP kB → eV/ų stress conversion).
• Generate a complete MACE finetune configuration: creates finetune_config.yaml compatible with mace_run_train, including options for freezing the backbone, reinitializing readout, and multi-head fine-tuning.
• Benchmark and validate during the loop: supports running training, then extracting standardized training history metrics for comparison to foundation performance.

Quick Start

Use the ml-mace-finetune skill to fine-tune a foundation MACE model by asking it to run data preparation from your labeled JSON, generate finetune_config.yaml for your train/validation .xyz files, and execute mace_run_train on your chosen GPU device.