Off-Lattice Markov Chain Monte Carlo Simulations of Mechanically Driven Polymers.

IF 5.7 1区化学 Q2 CHEMISTRY, PHYSICAL

Journal of Chemical Theory and Computation Pub Date : 2024-11-21 DOI:10.1021/acs.jctc.4c01260

Lijie Ding, Chi-Huan Tung, Bobby G Sumpter, Wei-Ren Chen, Changwoo Do

引用次数: 0

Abstract

We develop off-lattice simulations of semiflexible polymer chains subjected to applied mechanical forces by using Markov Chain Monte Carlo. Our approach models the polymer as a chain of fixed length bonds, with configurations updated through adaptive nonlocal Monte Carlo moves. This proposed method enables precise calculation of a polymer's response to a wide range of mechanical forces, which traditional on-lattice models cannot achieve. Our approach has shown excellent agreement with theoretical predictions of persistence length and end-to-end distance in quiescent states as well as stretching distances under tension. Moreover, our model eliminates the orientational bias present in on-lattice models, which significantly impacts calculations such as the scattering function, a crucial technique for revealing the polymer conformation.

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机械驱动聚合物的格外马尔可夫链蒙特卡罗模拟。

我们利用马尔可夫链蒙特卡洛（Markov Chain Monte Carlo）技术，对受到外加机械力作用的半柔性聚合物链进行了离格模拟。我们的方法将聚合物建模为固定长度的键链，并通过自适应非局部蒙特卡洛移动更新配置。这种方法可以精确计算聚合物对各种机械力的响应，而传统的晶格模型则无法做到这一点。我们的方法与理论预测的静止状态下的持续长度和端到端距离以及拉伸状态下的拉伸距离非常吻合。此外，我们的模型还消除了晶格上模型中存在的取向偏差，这种偏差会对散射函数等计算产生重大影响，而散射函数是揭示聚合物构象的关键技术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Theory and Computation 化学-物理：原子、分子和化学物理

CiteScore

9.90

自引率

16.40%

发文量

568

审稿时长

1 months

期刊介绍： The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.