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 l = 1 equivariant many-body interactions to enhance directional symmetric information in the message passing scheme. We provided a mathematical analysis demonstrating the necessity of incorporating many-body equivariant interactions and generalized the formulation to N-body interactions. Experiments indicate that integrating many-body equivariant representations enhances prediction accuracy across diverse scalar and tensorial quantum chemical properties.
@article{arxiv.2406.13265,
title = {Molecule Graph Networks with Many-body Equivariant Interactions},
author = {Zetian Mao and Chuan-Shen Hu and Jiawen Li and Chen Liang and Diptesh Das and Masato Sumita and Kelin Xia and Koji Tsuda},
journal= {arXiv preprint arXiv:2406.13265},
year = {2025}
}