MARA用连续球注意力提升分子力场的精度与稳定性
MARA: Continuous SE(3)-Equivariant Attention for Molecular Force Fields
- 提出SE(3)等变的连续球注意力机制，直接处理原子角距坐标
- 在多个分子基准上提升能量/力预测精度，降低高误差事件
- 可即插即用集成到MACE等模型，无需修改架构
机器学习力场（MLFF）已成为精确高效原子建模的关键。尽管精度高，现有方法多依赖固定角度展开，限制了对局部几何相互作用的灵活加权。本文提出模块化角-径向注意力（MARA），将原本用于SO(3)任务的球面注意力扩展至分子领域和SE(3)群，提供了一种高效的等变交互近似。MARA直接作用于邻近原子的角坐标与径向坐标，实现灵活、几何感知且模块化的局部环境加权。与现有SE(3)等变架构中的注意力机制不同，MARA可无修改地即插即用集成至MACE等模型。在多个分子基准测试中，MARA提升了能量与力的预测性能，减少了高误差事件并增强了鲁棒性。结果表明，连续球面注意力是一种有效且通用的几何算子，显著提升了原子模型的表达能力、稳定性和可靠性。
原文摘要 · Abstract (English)
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.
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