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We present a natural extension to E(n)-equivariant graph neural networks thatuses multiple equivariant vectors per node. We formulate the extension and showthat it improves performance across different physical systems benchmark tasks,with minimal differences in runtime or number of parameters. The proposedmultichannel EGNN outperforms the standard singlechannel EGNN on N-body chargedparticle dynamics, molecular property predictions, and predicting thetrajectories of solar system bodies. Given the additional benefits and minimaladditional cost of multi-channel EGNN, we suggest that this extension may be ofpractical use to researchers working in machine learning for the physicalsciences

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