graph-pes 0.0.8

Potential Energy Surfaces on Graphs

graph-pes is a framework built to accelerate the development of machine-learned potential energy surface (PES) models that act on graph representations of atomic structures.

Links: Google Colab Quickstart - Documentation - PyPI

Features

• Experiment with new model architectures by inheriting from our GraphPESModel base class.
• Train your own or existing models (e.g., SchNet, NequIP, PaiNN, MACE, etc.).
• Easily configure distributed training, learning rate scheduling, weights and biases logging, and other features using our graph-pes-train command line interface.
• Use our data-loading pipeline within your own training loop.
• Run molecular dynamics simulations via LAMMPS (or ASE) using any GraphPESModel and the pair_style graph_pes LAMMPS command.

Quickstart

pip install graph-pes
wget https://tinyurl.com/graph-pes-quickstart-cgap17
graph-pes-train quickstart-cgap17.yaml

Alternatively, for a 0-install quickstart experience, please see this Google Colab, which you can also find in our documentation.

Contributing

Contributions are welcome! If you find any issues or have suggestions for new features, please open an issue or submit a pull request on the GitHub repository.