Message passing neural networks have demonstrated significant efficacy in
predicting molecular interactions. Introducing equivariant vectorial
representations augments expressivity by capturing geometric data symmetries,
thereby improving model accuracy. However, two-body bond vectors in opposition
may cancel each other out during message passing, leading to the loss of
directional information on their shared node. In this study, we develop
Equivariant N-body Interaction Networks (ENINet) that explicitly integrates
equivariant many-body interactions to preserve directional information in the
message passing scheme. Experiments indicate that integrating many-body
equivariant representations enhances prediction accuracy across diverse scalar
and tensorial quantum chemical properties. Ablation studies show an average
performance improvement of 7.9% across 11 out of 12 properties in QM9, 27.9% in
forces in MD17, and 11.3% in polarizabilities (CCSD) in QM7b.

通过引入等变向量表示，消息传递神经网络在预测分子相互作用方面表现出显著的功效。在消息传递过程中，两个相对的化学键向量可能会相互抵消，导致他们共享节点上的方向信息丢失。本研究开发了等变 N 体互作用网络（ENINet），明确地整合等变的多体互作用以保留消息传递方案中的方向信息。实验证明，整合多体等变表示增强了对不同标量和张量量子化学性质的预测准确性。消融研究显示，在 QM9 的 12 个性质中有 11 个平均性能提升了 7.9%，在 MD17 的力中提升了 27.9%，在 QM7b 的极化率（CCSD）中提升了 11.3%。