Graph Neural Network with Local Frame for Molecular Potential Energy Surface

Xiyuan Wang, Muhan Zhang

Keywords: Potential Energy Surface, GNN

TL;DR: A state-of-the-art Graph Neural Network for molecular potential energy surface. It removes existing complicated design, achieves high theoretical expressivity, and outperforms baselines in accuracy and efficiency.

Abstract: Modeling molecular potential energy surface is of pivotal importance in science. Graph Neural Networks have shown great success in this field. However, their message passing schemes need special designs to capture geometric information and fulfill symmetry requirement like rotation equivariance, leading to complicated architectures. To avoid these designs, we introduce a novel local frame method to molecule representation learning and analyze its expressivity. Projected onto a frame, equivariant features like 3D coordinates are converted to invariant features, so that we can capture geometric information with these projections and decouple the symmetry requirement from GNN design. Theoretically, we prove that given non-degenerate frames, even ordinary GNNs can encode molecules injectively and reach maximum expressivity with coordinate projection and frame-frame projection. In experiments, our model uses a simple ordinary GNN architecture yet achieves state-of-the-art accuracy. The simpler architecture also leads to higher scalability. Our model only takes about $30\%$ inference time and $10\%$ GPU memory compared to the most efficient baselines.

Type Of Submission: Full paper proceedings track submission (max 9 main pages).

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Type Of Submission: Full paper proceedings track submission.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2208.00716/code)