Geometric GNNs are an emerging class of GNNs for spatially embedded graphs in scientific and engineering applications, s.a. biomolecular structure, material science, and physical simulations. Notable examples include SchNet, DimeNet, Tensor Field Networks, and E(n) Equivariant GNNs.

https://github.com/chaitjo/geometric-gnn-dojo/blob/main/geom...

This notebook and repository aims to serve as a 'Geometric GNNs 101' introduction for newcomers.

We walk through the basics of GNNs, Geometric Deep Learning, and the PyTorch Geometric library for implementing these concepts.

Our goal is to help students understand how theory/equations connect to real code.