# Geometric Graph Neural Diffusion (GGND) for Generalizable Force Field

This repository contains the implementation of the **Geometric Graph Neural Diffusion (GGND)** model integrated with **MACE** for a generalizable molecular force field, as presented in the paper *"Geometric Graph Neural Diffusion for Generalizable Force Field"*.

## Datasets

Download and place the following datasets in the `./datasets` directory:

- **3BPA**: Available at [https://pubs.acs.org/doi/10.1021/acs.jctc.1c00647](https://pubs.acs.org/doi/10.1021/acs.jctc.1c00647)
- **SAMD23**: Available at [https://github.com/SAITPublic/MLFF-Framework](https://github.com/SAITPublic/MLFF-Framework)

Ensure the datasets are properly formatted and extracted before training.

## Environment Setup

The codebase builds on the **MACE** framework. Follow the environment setup instructions provided in the MACE repository: [https://github.com/ACEsuit/mace](https://github.com/ACEsuit/mace).

### Prerequisites

- Python 3.10+
- Dependencies listed in the MACE repository (e.g., PyTorch, NumPy, etc.)
- Ensure a compatible CUDA version for GPU acceleration (if applicable)

## Training

To train the GGND model on the 3BPA dataset, run the following command:

```bash
sh scripts/train-3BPA.sh
```

Additional training scripts for other datasets (e.g., SAMD23) can be found in the `scripts/` directory.