题目 Title

Coarse-Grained Mapping of Fluid Particles via Evolutionary Fuzzy Clustering: Membership-Evolution Term as a Pressure Correction Mechanism

期刊 Journal

Journal of Chemical Theory and Computation (IF=6.4)

作者 Author

Han, J. L.; Feng, Y. X.; Wu, J.; Fang, H. W.

摘要 Abstract

Coarse-graining is an effective approach for bridging atomistic and mesoscopic descriptions of fluid particle systems. However, fixed coarse-grained (CG) mappings do not account for the unbundled nature of fluid particles. We propose an entropy-regularized fuzzy clustering method with temporal smoothness constraints, examining in detail the role of the evolution of fuzzy particle–cluster membership degrees throughout the coarse-graining process. Entropy regularization controls the level of spatial fuzziness, while the temporal smoothness constraints enhance the continuity of cluster position evolution. Within a bottom-up force-matching framework, the interactions between clusters are decomposed into two contributions: a particle-interaction term, which is the weighted sum of interactions between particles, and a membership-evolution term, which originates from the temporal variation of membership degrees. Analyses based on the Lennard–Jones (L-J) fluid particle system and the water molecule system show that an intermediate level of fuzziness yields the most pronounced structural features in the radial distribution functions. The particle-interaction term exhibits system-dependent characteristics, whereas the membership-evolution term consistently provides a repulsive contribution across different systems. Moreover, CG dynamics simulations of the L-J fluid demonstrate that including the membership-evolution term effectively restores the system pressure, which could be interpreted as a pressure correction scheme. This finding provides a physical perspective on the transition from microscopic particle interactions to macroscopic fluid pressure constraints and reveals a bottom-up origin for incorporating additional pressure corrections into fluid CG dynamics, which could be beneficial for the future design of coarse-graining strategies.

简介 Brief introduction

对于流体粒子系统，粗粒化是连接分子尺度与流体尺度的重要工具。然而，固定的映射方式难以充分刻画流体粒子的分散特性。本文提出了一种引入时间平滑约束的熵正则化模糊聚类方法，具体分析粒子对团簇的模糊隶属度演化在粗粒化中的作用。该方法通过熵正则化控制了空间模糊程度，并通过时间平滑约束增强团簇中心位置演化的连续性。基于自下而上的力匹配思想，可将团簇间相互作用拆分为两部分：粒子间作用力隶属度加权求和得到的粒子作用力项与隶属度时变相关的隶属度演化项。基于 L-J 流体粒子体系与水分子体系的分析表明，适度的空间模糊程度能够最大化保留局部结构特征。粒子作用力项的特征依赖于体系，而隶属度演化项在不同体系中统一表现为排斥作用为主。对 L-J 粒子体系的团簇动力学模拟进一步发现，包含隶属度演化项有效恢复了体系压强。这一发现提供了流体从微观相互作用到宏观压强约束过渡的物理视角，并揭示了在流体粗粒化模拟中包含额外压强修正机制的自下而上物理来源解释，为未来流体粗粒化方案的设计提供了思路。

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