Equivariant Spherical Transformer for Efficient Molecular Modeling

• URL: http://arxiv.org/abs/2505.23086v1
• Date: Thu, 29 May 2025 04:43:07 GMT
• Title: Equivariant Spherical Transformer for Efficient Molecular Modeling
• Authors: Junyi An, Xinyu Lu, Chao Qu, Yunfei Shi, Peijia Lin, Qianwei Tang, Licheng Xu, Fenglei Cao, Yuan Qi,
• Abstract summary: We introduce the Equivariant Spherical Transformer (EST), a novel framework that leverages a Transformer structure within the spatial domain of group representations.<n>EST can encompass the function space of tensor products while achieving superior expressiveness.<n>Our experiments demonstrate state-of-the-art performance by EST on various molecular benchmarks, including OC20 and QM9.
• Score: 6.616200165174097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: SE(3)-equivariant Graph Neural Networks (GNNs) have significantly advanced molecular system modeling by employing group representations. However, their message passing processes, which rely on tensor product-based convolutions, are limited by insufficient non-linearity and incomplete group representations, thereby restricting expressiveness. To overcome these limitations, we introduce the Equivariant Spherical Transformer (EST), a novel framework that leverages a Transformer structure within the spatial domain of group representations after Fourier transform. We theoretically and empirically demonstrate that EST can encompass the function space of tensor products while achieving superior expressiveness. Furthermore, EST's equivariant inductive bias is guaranteed through a uniform sampling strategy for the Fourier transform. Our experiments demonstrate state-of-the-art performance by EST on various molecular benchmarks, including OC20 and QM9.

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