发表机构
University of Wisconsin - Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出将几何与方向性纳入粗粒度机器学习势的通用框架，通过AniSOAP与MACE-CG两种方法，提升了能量、力和力矩的预测性能，为构建更优的粗粒度机器学习势提供了系统途径。
AI 中文摘要
机器学习原子间势已能实现高精度的原子模拟，但由于粗粒化过程中几何与取向信息的丢失，将该能力扩展至粗粒度系统仍具挑战性。本研究提出一种通用框架，通过两种互补方法将分子几何与方向性纳入粗粒度机器学习势：各向异性基于密度的描述符（AniSOAP）和对称适配等变消息传递神经网络（MACE-CG）。使用Gay-Berne粒子及苯、甲酰胺、水的粗粒度表示，我们证明与各向同性表示相比，显式保留分子各向异性可显著提升能量、力和力矩的预测性能。当分子形状可由椭球对称性良好近似时，AniSOAP提供有效的线性基线；而对称适配MACE-CG则能纳入任意分子点群对称性。对于取向自由度难以仅由椭球描述符表示的水，对称适配刚体特征通过解决各向同性及基于转动惯量的表示中固有的取向简并，提升了能量、力和力矩的预测性能。这些结果表明，粗粒度建模中的信息损失不仅取决于映射分辨率，还取决于表示中保留的对称性和几何信息，为构建更具表达力和可迁移性的粗粒度机器学习势提供了系统途径。
英文摘要
Machine-learned interatomic potentials have enabled highly accurate atomistic simulations, but extending these capabilities to coarse-grained systems remains challenging due to the loss of geometric and orientational information during coarse-graining. In this work, we present a generalized framework for incorporating molecular geometry and directionality into coarse-grained machine-learned potentials through two complementary approaches: anisotropic density-based descriptors (AniSOAP) and symmetry-adapted equivariant message-passing neural networks (MACE-CG). Using Gay-Berne particles and coarse-grained representations of benzene, formamide, and water, we demonstrate that explicitly retaining molecular anisotropy substantially improves the prediction of energies, forces, and torques relative to isotropic representations. AniSOAP provides an effective linear baseline when molecular shape is well approximated by ellipsoidal symmetry, while symmetry-adapted MACE-CG enables the incorporation of arbitrary molecular point-group symmetries. For water, whose orientational degrees of freedom are poorly represented by ellipsoidal descriptors alone, symmetry-adapted rigid-body features improve energy, force, and torque prediction by resolving orientational degeneracies inherent to isotropic and moment-of-inertia-based representations. These results show that information loss in coarse-grained modeling is governed not only by mapping resolution but also by the symmetry and geometric information retained in the representation, providing a systematic route toward more expressive and transferable coarse-grained machine-learned potentials.
