Molecule Graph Networks with Many-body Equivariant Interactions
This work addresses a specific bottleneck in molecular property prediction for computational chemistry, representing an incremental improvement over existing equivariant methods.
The authors tackled the problem of directional information loss in message passing neural networks for molecular interactions by developing ENINet, which integrates many-body equivariant interactions, resulting in enhanced prediction accuracy for scalar and tensorial quantum chemical properties.
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.