Lightweight Geometric Deep Learning for Molecular Modelling in Catalyst
  Discovery
Patrick Geitner
New technology for energy storage is necessary for the large-scale adoption
of renewable energy sources like wind and solar. The ability to discover
suitable catalysts is crucial for making energy storage more cost-effective and
scalable. The Open Catalyst Project aims to apply advances in graph neural
networks (GNNs) to accelerate progress in catalyst discovery, replacing Density
Functional Theory-based (DFT) approaches that are computationally burdensome.
Current approaches involve scaling GNNs to over 1 billion parameters, pushing
the problem out of reach for a vast majority of machine learning practitioner
around the world. This study aims to evaluate the performance and insights
gained from using more lightweight approaches for this task that are more
approachable for smaller teams to encourage participation from individuals from
diverse backgrounds. By implementing robust design patterns like geometric and
symmetric message passing, we were able to train a GNN model that reached a MAE
of 0.0748 in predicting the per-atom forces of adsorbate-surface interactions,
rivaling established model architectures like SchNet and DimeNet++ while using
only a fraction of trainable parameters.