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多体相互作用在粗粒聚合物结构中的作用。

Role of many-body interactions in the structure of coarse-grained polymers.

机构信息

NRC Research Associate, Resident at Center for Computational Materials Science, Naval Research Laboratory, Washington, DC 20375.

US Naval Research Laboratory, 4555 Overlook Avenue SW, Washington, DC 20375.

出版信息

Phys Rev E. 2018 Aug;98(2-1):023310. doi: 10.1103/PhysRevE.98.023310.

Abstract

In developing coarse-grained (CG) polymer models it is important to reproduce both local and molecule-scale structure. We develop a procedure for fast calculation of the bond-orientation correlation and the internal squared distance 〈R^{2}(M)〉 through evaluation of the probability distribution functions that represent a CG model. Different CG models inherently contain or omit correlations between CG variables. Here, we construct CG models that contain specific correlations between CG variables. The importance of different correlations is tested on CG models of polyethylene, polytetrafluoroethylene, and poly-L-lactic acid. The chain stiffness and 〈R^{2}(M)〉 are calculated using both analytic evaluation and Monte Carlo sampling, and approximate model results are compared with exact results from all-atom simulations. For polymers with an exponential correlation decay, the bond-orientation correlation and 〈R^{2}(M)〉 indicate which CG variable correlations are most important to reproduce molecule-scale structure. Analysis of the bond-orientation correlation and internal-squared distance indicates that for poly-L-lactic acid the bond-orientation correlation requires qualitatively different additional terms in CG models and quantifies the error in neglecting this important behavior.

摘要

在开发粗粒度(CG)聚合物模型时,重要的是要再现局部和分子尺度的结构。我们开发了一种通过评估表示 CG 模型的概率分布函数来快速计算键取向相关和内部平方距离〈R^{2}(M)〉的方法。不同的 CG 模型本质上包含或省略 CG 变量之间的相关性。在这里,我们构建了包含 CG 变量之间特定相关性的 CG 模型。在聚乙烯、聚四氟乙烯和聚 L-乳酸的 CG 模型上测试了不同相关性的重要性。使用解析评估和蒙特卡罗采样计算链刚度和〈R^{2}(M)〉,并将近似模型结果与全原子模拟的精确结果进行比较。对于具有指数相关衰减的聚合物,键取向相关和〈R^{2}(M)〉表明哪些 CG 变量相关性对于再现分子尺度的结构最重要。对键取向相关和内部平方距离的分析表明,对于聚 L-乳酸,CG 模型需要定性地引入不同的附加项来描述键取向相关,并且量化了忽略这种重要行为的误差。

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