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基于微观模拟的水的半底向上粗粒化。

Semi-bottom-up coarse graining of water based on microscopic simulations.

机构信息

College of Chemistry, Beijing Normal University, Beijing 100875, China.

出版信息

J Chem Phys. 2011 Nov 14;135(18):184101. doi: 10.1063/1.3658500.

Abstract

The generalized dissipative particle dynamics (DPD) equation derived from the generalized Langevin equation under Markovian approximations is used to simulate coarse-grained (CG) water cells. The mean force and the friction coefficients in the radial and transverse directions needed for DPD equation are obtained directly from the all atomistic molecular dynamics (AAMD) simulations. But the dissipative friction forces are overestimated in the Markovian approximation, which results in wrong dynamic properties for the CG water in the DPD simulations. To account for the non-Markovian dynamics, a rescaling factor is introduced to the friction coefficients. The value of the factor is estimated by matching the diffusivity of water. With this semi-bottom-up mapping method, the radial distribution function, the diffusion constant, and the viscosity of the coarse-grained water system computed with DPD simulations are all in good agreement with AAMD results. It bridges the microscopic level and mesoscopic level with consistent length and time scales.

摘要

从 Markov 近似下的广义朗之万方程推导出的广义耗散粒子动力学(DPD)方程被用于模拟粗粒化(CG)水单元。DPD 方程所需的径向和横向平均力和摩擦系数直接从全原子分子动力学(AAMD)模拟中获得。但是,Markov 近似中耗散摩擦力被高估,导致 DPD 模拟中的 CG 水的动态特性错误。为了考虑非 Markov 动力学,引入了耗散摩擦系数的调整因子。该因子的值通过匹配水的扩散系数来估计。使用这种自下而上的映射方法,用 DPD 模拟计算的粗粒化水系统的径向分布函数、扩散常数和粘度都与 AAMD 结果非常吻合。它在一致的长度和时间尺度上连接了微观和介观水平。

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