Molecule Graph Networks with Many-body Equivariant Interactions
<|reference_start|>Molecule Graph Networks with Many-body Equivariant Interactions: 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.<|reference_end|>
@article{mao2024molecule,
    title={Molecule Graph Networks with Many-body Equivariant Interactions},
    author={Zetian Mao, Jiawen Li, Chen Liang, Diptesh Das, Masato Sumita, Koji
  Tsuda},
    journal={arXiv preprint arXiv:2406.13265},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13265},
    primaryClass={cs.LG cond-mat.mtrl-sci}
}