Computer Science > Machine Learning
[Submitted on 19 Jun 2024 (v1), last revised 22 Jan 2025 (this version, v2)]
Title:Molecule Graph Networks with Many-body Equivariant Interactions
View PDF HTML (experimental)Abstract: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.
Submission history
From: Zetian Mao [view email][v1] Wed, 19 Jun 2024 06:53:09 UTC (722 KB)
[v2] Wed, 22 Jan 2025 02:06:59 UTC (786 KB)
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