SE(3) Equivariant Neural Network for 3D Graphs
Sarp Aykent, Tian Xia · 2024
Three-dimensional (3D) graph representations have gained significant importance in numerous scientific domains, such as molecular dynamics and astrophysics. In these applications, a precise and effective representation of 3D graphs is crucial. Although various neural network architectures have been proposed to encapsulate the intricate relationships inherent to these graphs, many remain sensitive to challenges such as reflection variances observed in molecular structures. In this paper, we present an SE(3) equivariant neural network architecture tailored for 3D graphs. Our approach uniquely integrates equivariance to both rotations and translations, ensuring robustness against these spatial variances. Distinctively, our model leverages an innovative message passing mechanism, endowing it with an intrinsic understanding of reflection variances. This mechanism not only facilitates superior 3D graph representation but also promotes computational efficiency. Unlike many existing methods, our architecture sidesteps the need for computationally demanding higher-order representations in intermediary layers while still achieving competitive or superior performance metrics. Empirical evaluations conducted on synthetic and real-world datasets underscore the superior performance of our approach in comparison to prevalent models in terms of both accuracy and efficiency.