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

我们提出了 E (n)- 等变图神经网络的一个自然扩展，它使用每个节点的多个等变向量。我们阐述了这种扩展，并展示了它在不同物理系统基准任务上提高了性能，同时几乎没有运行时间或参数数量上的差异。该多通道 EGNN 在 N 体带电粒子动力学、分子属性预测和太阳系物体轨迹预测方面优于标准的单通道 EGNN。考虑到多通道 EGNN 的额外优点和最小成本，我们建议这种扩展可能对从事物理科学机器学习研究的研究人员有实际用途。