Using Multiple Vector Channels Improves $E(n)$-Equivariant Graph Neural Networks

We present a natural extension to $E(n)$-equivariant graph neural networks that uses multiple equivariant vectors per node. We formulate the extension and show that it improves performance across different physical systems benchmark tasks, with minimal differences in runtime or number of parameters. The proposed multi-channel EGNN outperforms the standard single-channel EGNN on N-body charged particle dynamics, molecular property predictions, and predicting the trajectories of solar system bodies. Given the additional benefits and minimal additional cost of multi-channel EGNN, we suggest that this extension may be of practical use to researchers working in machine learning for astrophysics and cosmology.