Bottom-up coarse-graining of polymers is commonly performed by matching structural order parameters such as distribution of bond lengths, bending and dihedral angles, and pair distribution functions. In this study, we introduce the distribution of nearest-neighbors as an additional order parameter in the concept of local density potentials. We describe how the inverse-Monte Carlo method provides a framework for forcefield development that is capable of overcoming challenges associated with the parameterization of interaction terms in polymer systems. The technique is applied on polyisoprene melts as a prototype system. We demonstrate that while different forcefields can be developed that perform equally in terms of matching target distributions, the inclusion of nearest-neighbors provides a straightforward route to match both thermodynamic and conformational properties. We find that several temperature state points can also be addressed, provided that the forcefield is refined accordingly. Finally, we examine both the single-particle and the collective dynamics of the coarse-grain models, demonstrating that all forcefields present a similar acceleration relative to the atomistic systems.

中文翻译：

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使用反向蒙特卡洛和局部密度势对聚异戊二烯熔体进行粗粒化

聚合物的自下而上的粗粒度通常通过匹配结构顺序参数（例如键长分布，弯曲角和二面角以及成对分布函数）来执行。在这项研究中，我们在局部密度势的概念中引入了最近邻的分布作为附加的阶次参数。我们描述了反向蒙特卡罗方法如何为力场发展提供一个框架，该框架能够克服与聚合物系统中相互作用项的参数化相关的挑战。该技术作为原型系统应用于聚异戊二烯熔体。我们证明，虽然可以开发出在匹配目标分布方面表现均等的不同力场，最近邻的加入为匹配热力学和构象性质提供了一条直接途径。我们发现，只要相应地完善了力场，也可以解决几个温度状态点。最后，我们检查了粗粒模型的单粒子动力学和集体动力学，证明了所有力场相对于原子系统都表现出相似的加速度。