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无规聚合物的均方末端距、末端距分布和链段长度

Persistence Length, End-to-End Distance, and Structure of Coarse-Grained Polymers.

机构信息

NRC Research Associate, Resident at Center for Computational Materials Science , US Naval Research Laboratory , Washington, D.C. 20375 , United States.

US Naval Research Laboratory , 4555 Overlook Ave SW , Washington, D.C. 20375 , United States.

出版信息

J Chem Theory Comput. 2018 Apr 10;14(4):2219-2229. doi: 10.1021/acs.jctc.7b01229. Epub 2018 Mar 29.

Abstract

Coarse-grained (CG) polymer simulations can access longer times and larger lengths than all-atom (AA) molecular dynamics simulations; however, not all CG models correctly reproduce polymer properties on all length scales. Here we coarse-grain atomistic position data from polyethylene (PE) and polytetrafluoroethylene (PTFE) melt simulations by combining λ backbone carbon atoms in a single CG bead. Resulting CG variables have correlations along the chain backbone that depend on the coarse-graining scale λ and are generally not reproduced by independent bond-length, bond-angle and torsion-angle distributions. By constructing distributions of CG variables equivalent to those from simulated CG potentials we are able to evaluate the bond-orientation correlation for different CG models at reduced computational cost. CG models and potentials that include only nonbonded, bond-length, and bond-angle interactions computed by Boltzmann inversion correctly reproduce the CG variable distributions but do not necessarily reproduce the chain stiffness, overestimating the persistence length L and end-to-end distance ⟨ R⟩ with increasing λ. While CG models that include an independent torsion angle match the bond-orientation correlation and ⟨ R⟩ better, only approximate models that include correlations between bond and torsion angles match the true bond-orientation correlation.

摘要

粗粒化(CG)聚合物模拟可以比全原子(AA)分子动力学模拟访问更长的时间和更大的长度;然而,并非所有 CG 模型都能在所有长度尺度上正确再现聚合物性质。在这里,我们通过将单个 CG 珠中的 λ 主链碳原子组合,对聚乙烯(PE)和聚四氟乙烯(PTFE)熔体模拟的原子位置数据进行粗粒化。得到的 CG 变量沿着链主链具有相关性,这取决于粗粒化尺度 λ,并且通常不能由独立的键长、键角和扭转角分布再现。通过构建与模拟 CG 势能相同的 CG 变量分布,我们能够以降低的计算成本评估不同 CG 模型的键取向相关性。仅包含非键合、键长和键角相互作用的 CG 模型和势能通过玻尔兹曼反演计算,可以正确再现 CG 变量分布,但不一定再现链刚度,随着 λ 的增加高估了无规线团长度 L 和末端到末端距离 ⟨R⟩。虽然包含独立扭转角的 CG 模型更好地匹配键取向相关性和 ⟨R⟩,但只有包含键和扭转角之间相关性的近似模型才能匹配真实的键取向相关性。

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