Relaxed Equivariant Graph Neural Networks

Abstract

3D Euclidean symmetry equivariant neural networks have demonstrated notable success in modeling complex physical systems. We introduce a framework for relaxed $E(3)$ graph equivariant neural networks that can learn and represent symmetry breaking within continuous groups. Building on the existing e3nn framework, we propose the use of relaxed weights to allow for controlled symmetry breaking. We show empirically that these relaxed weights learn the correct amount of symmetry breaking.

Extended abstract presented at the Geometry-grounded Representation Learning and Generative Modeling Workshop (GRaM) at the 41st International Conference on Machine Learning, July 2024, Vienna, Austria