This didactic repository contains example code for desiging message-passing layers on graph and hypergraph structures.
It was prepared for a datacraft state-of-the-art session on Message-Passing Neural Networks for Generation of Chemical Structures (slides).
With python 3.9.18:
$ git clone git@github.com:opeltre/gnn && cd gnn
$ pip install -r requirements.txt && pip install -e .
With python 3.12, installation of torch-scatter
may fail depending on the torch
version.
A working requirements.txt
or pyproject.toml
should be uploaded!
- Equivariant Message Passing Neural Network for Crystal Material Discovery (Klipfel et al 2023)
- MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields (Batatia et al 2022)
- HamGNN:Transferable equivariant graph neural networks for the Hamiltonians of molecules and solids(Zhong et al 2023)
- Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds (Thomas et al 2018)
- A Gentle Introduction to Graph Neural Networks
- A Hitchhiker’s Guide to Geometric GNNs for 3D Atomic Systems
The QM9 dataset contains ~130k small organic molecules, its upstream url is quantum-machine.org.
An interface to QM9 ships with torch_geometric
, see QM9
and examples/graph_mpnn.py
Find the QM7 dataset and its description from quantum-machine.org:
export GNN_DATA=".data"
curl http://quantum-machine.org/data/qm7.mat > $GNN_DATA/qm7.mat