School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.
Accelerated Materials Design and Discovery, Toyota Research Institute, Los Altos, California 94022, United States.
J Chem Theory Comput. 2022 Apr 12;18(4):2737-2748. doi: 10.1021/acs.jctc.2c00022. Epub 2022 Mar 4.
Three-dimensional atomic-level models of polymers are the starting points for physics-based simulation studies. A capability to generate reasonable initial structural models is highly desired for this purpose. We have developed a python toolkit, namely, polymer structure predictor (psp), to generate a hierarchy of polymer models, ranging from oligomers to infinite chains to crystals to amorphous models, using a simplified molecular-input line-entry system (SMILES) string of the polymer repeat unit as the primary input. This toolkit allows users to tune several parameters to manage the quality and scale of models and computational cost. The output structures and accompanying force field (GAFF2/OPLS-AA) parameter files can be used for downstream and molecular dynamics simulations. The psp package includes a Colab notebook where users can go through several examples, building their own models, visualizing them, and downloading them for later use. The psp toolkit, being a first of its kind, will facilitate automation in polymer property prediction and design.
聚合物的三维原子级模型是基于物理模拟研究的起点。为此，非常需要能够生成合理初始结构模型的能力。我们开发了一个名为聚合物结构预测器（psp）的 Python 工具包，该工具包使用聚合物重复单元的简化分子输入行输入系统（SMILES）字符串作为主要输入，生成从低聚物到无限链到晶体到无定形模型的聚合物模型层次结构。该工具包允许用户调整几个参数来管理模型的质量和规模以及计算成本。输出结构和伴随的力场（GAFF2/OPLS-AA）参数文件可用于下游和分子动力学模拟。psp 包包含一个 Colab 笔记本，用户可以在其中浏览几个示例，构建自己的模型，对其进行可视化，并下载以供以后使用。psp 工具包是同类产品中的首创，将促进聚合物性质预测和设计的自动化。
