MatGL Fine-tuning

Goal

To evaluate and improve the accuracy of a foundation MatGL potential (e.g., CHGNet, M3GNet, TensorNet) for a specific chemical system or physical property using the provided Python fine-tuning script.

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. Data Preparation: Execute scripts/prepare_matgl_data.py to process JSON structures and split into training and validation sets.
4. Fine-Tuning: Execute scripts/train_matgl.py to begin fine-tuning natively on the GPU using PyTorch Lightning.
5. Validation: Verify convergence and compare against the benchmarked foundation metrics.
6. 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.

Usage