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Simu-D：用于极端条件下基于聚合物的系统的模拟器描述符套件。

Simu-D: A Simulator-Descriptor Suite for Polymer-Based Systems under Extreme Conditions.

机构信息

Institute for Optoelectronic Systems and Microtechnology (ISOM) and Escuela Técnica Superior de Ingenieros Industriales (ETSII), Universidad Politécnica de Madrid (UPM), José Gutierrez Abascal 2, 28006 Madrid, Spain.

出版信息

Int J Mol Sci. 2021 Nov 18;22(22):12464. doi: 10.3390/ijms222212464.

DOI:10.3390/ijms222212464
PMID:34830346
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8621175/
Abstract

We present Simu-D, a software suite for the simulation and successive identification of local structures of atomistic systems, based on polymers, under extreme conditions, in the bulk, on surfaces, and at interfaces. The protocol is built around various types of Monte Carlo algorithms, which include localized, chain-connectivity-altering, identity-exchange, and cluster-based moves. The approach focuses on alleviating one of the main disadvantages of Monte Carlo algorithms, which is the general applicability under a wide range of conditions. Present applications include polymer-based nanocomposites with nanofillers in the form of cylinders and spheres of varied concentration and size, extremely confined and maximally packed assemblies in two and three dimensions, and terminally grafted macromolecules. The main simulator is accompanied by a descriptor that identifies the similarity of computer-generated configurations with respect to reference crystals in two or three dimensions. The Simu-D simulator-descriptor can be an especially useful tool in the modeling studies of the entropy- and energy-driven phase transition, adsorption, and self-organization of polymer-based systems under a variety of conditions.

摘要

我们提出了 Simu-D，这是一套用于模拟和连续识别原子系统局部结构的软件套件，涵盖了聚合物在极端条件下的本体、表面和界面情况。该方案围绕各种类型的蒙特卡罗算法构建，其中包括局部化、链连接改变、身份交换和基于簇的移动。该方法侧重于缓解蒙特卡罗算法的一个主要缺点，即在广泛条件下的普遍适用性。目前的应用包括以圆柱和球体形式存在的纳米纤维的聚合物基纳米复合材料，具有不同浓度和尺寸的极度受限和最大堆积的二维和三维组装，以及末端接枝的大分子。主要的模拟器配有一个描述符，用于识别二维或三维参考晶体的计算机生成构型的相似性。Simu-D 模拟器-描述符在各种条件下的聚合物基系统的熵和能量驱动的相转变、吸附和自组织的建模研究中可能是一个特别有用的工具。

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