标题： 图粗化用于机器学习粗粒分子动力学 显示英文标题

标题： Graph-Coarsening for Machine Learning Coarse-grained Molecular Dynamics

摘要： 粗粒度（CG）分子动力学（MD）模拟通过减少自由度，可以在长时间尺度上模拟大型分子复合物。 CG建模中的关键步骤是选择CG映射算法，这直接影响模型的准确性和可解释性。 尽管取得了进展，最佳的粗粒化策略仍然是一个具有挑战性的任务，突显了建立全面理论框架的必要性。 在本工作中，我们提出了一种基于图的粗粒化方法来开发CG模型。 通过边收缩获得粗粒度位点，其中节点根据局部变分成本度量进行合并，同时保留原始图的关键谱特性。 此外，我们说明了如何应用消息传递原子簇展开（MACE）来生成不仅高效而且准确的ML-CG势能。 我们的方法为开发系统可改进的CG势能提供了一种自下而上的、理论基础坚实的计算方法。 显示英文摘要

摘要： Coarse-grained (CG) molecular dynamics (MD) simulations can simulate large molecular complexes over extended timescales by reducing degrees of freedom. A critical step in CG modeling is the selection of the CG mapping algorithm, which directly influences both accuracy and interpretability of the model. Despite progress, the optimal strategy for coarse-graining remains a challenging task, highlighting the necessity for a comprehensive theoretical framework. In this work, we present a graph-based coarsening approach to develop CG models. Coarse-grained sites are obtained through edge contractions, where nodes are merged based on a local variational cost metric while preserving key spectral properties of the original graph. Furthermore, we illustrate how Message Passing Atomic Cluster Expansion (MACE) can be applied to generate ML-CG potentials that are not only highly efficient but also accurate. Our approach provides a bottom-up, theoretically grounded computational method for the development of systematically improvable CG potentials.