Using Multiple Vector Channels Improves E(n)-Equivariant Graph Neural
  Networks
Daniel Levy Sékou-Oumar Kaba Carmelo Gonzales Santiago Miret Siamak Ravanbakhsh
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
multichannel EGNN outperforms the standard singlechannel 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 the physical
sciences