Equivariant Spherical Transformer for Efficient Molecular Modeling

The paper introduces the Equivariant Spherical Transformer (EST), a novel framework that enhances the expressiveness of SE(3)-equivariant Graph Neural Networks by integrating Transformer architecture within the Fourier-transformed group representation space. Empirical results on molecular benchmarks like OC20 and QM9 show that EST achieves state-of-the-art performance, overcoming limitations of previous tensor product-based convolutions.