MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields

TL;DR

MARA introduces a flexible, SE(3)-equivariant attention module for molecular force fields, enhancing accuracy, robustness, and expressiveness by directly operating on atomic coordinates and seamlessly integrating into existing models.

Contribution

The paper presents MARA, a novel modular attention mechanism for SE(3)-equivariant models, improving their ability to weight local atomic environments adaptively.

Findings

MARA improves energy and force prediction accuracy.

MARA reduces high-error events in molecular modeling.

MARA enhances model robustness and stability.

Abstract

Machine learning force fields (MLFFs) have become essential for accurate and efficient atomistic modeling. Despite their high accuracy, most existing approaches rely on fixed angular expansions, limiting flexibility in weighting local geometric interactions. We introduce Modular Angular-Radial Attention (MARA), a module that extends spherical attention -- originally developed for SO(3) tasks -- to the molecular domain and SE(3), providing an efficient approximation of equivariant interactions. MARA operates directly on the angular and radial coordinates of neighboring atoms, enabling flexible, geometrically informed, and modular weighting of local environments. Unlike existing attention mechanisms in SE(3)-equivariant architectures, MARA can be integrated in a plug-and-play manner into models such as MACE without architectural modifications. Across molecular benchmarks, MARA improves energy and force predictions, reduces high-error events, and enhances robustness. These results demonstrate that continuous spherical attention is an effective and generalizable geometric operator that increases the expressiveness, stability, and reliability of atomistic models.