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

2023/09/06 by Daniel Lévy, Levy, Daniel, Sékou-Oumar Kaba +7 · 3 citations
Computer Science · Materials Science · #Computational Physics and Python Applications #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2309.03139

openalex publication_date 2023/09/06 · openalex created_date 2023/09/09 · openalex updated_date 2026/07/28

Abstract

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

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