标题： 块状聚合物材料的逆向设计，以实现所需的纳米结构和宏观性能 显示英文标题

标题： Inverse Design of Block Polymer Materials with Desired Nanoscale Structure and Macroscale Properties

摘要： 通过精心设计具有定制材料特性的新型聚合物一直是该领域长期面临的挑战，这是由于可能的聚合物设计变量数量庞大。 为了加速这一设计过程，迫切需要开发新的工具来辅助逆向设计过程，并高效探索高维的聚合物设计空间。 对聚合物系统的宏观材料性能进行优化是困难的，因为性能由多个长度尺度上的特征决定，从所选单体化学性质到链级设计，再到更大尺度的域结构。 在本工作中，我们提出了一种高效的基于计算机模拟的高通量框架，以有效设计具有所需多尺度纳米结构和宏观性能的高性能聚合物，我们称之为RAPSIDY 2.0 - 聚合物结构快速分析与逆向设计策略2.0。 RAPSIDY的新版本建立在我们之前的工作RAPSIDY 1.0之上，后者纯粹专注于识别能够稳定所需纳米尺度形态的聚合物设计。 在RAPSIDY 2.0中，我们使用分子动力学模拟和贝叶斯优化驱动的主动学习相结合的方法，最优地查询高维聚合物设计空间，并提出同时稳定选定纳米尺度形态并表现出所需宏观材料性能的有前景的设计候选。 我们利用将聚合物链预先放置在选定纳米尺度形态中的MD模拟，并进行虚拟实验以确定所选聚合物设计在目标形态中的稳定性并计算所需的宏观材料性能 （例如，热导率）。 我们的方法直接解决了共聚物特有的挑战，其宏观性能是其链设计和介观尺度形态的函数，这两者是耦合的。 显示英文摘要

摘要： The rational design of novel polymers with tailored material properties has been a long-standing challenge in the field due to the large number of possible polymer design variables. To accelerate this design process, there is a critical need to develop novel tools to aid in the inverse design process and efficiently explore the high-dimensional polymer design space. Optimizing macroscale material properties for polymeric systems is difficult as properties are dictated by features on a multitude of length scales, ranging from the chosen monomer chemistries to the chain level design to larger-scale domain structures. In this work, we present an efficient high-throughput in-silico based framework to effectively design high-performance polymers with desired multi-scale nanostructure and macroscale properties, which we call RAPSIDY 2.0 - Rapid Analysis of Polymer Structure and Inverse Design strategY 2.0. This new version of RAPSIDY builds upon our previous work, RAPSIDY 1.0, which focused purely on identifying polymer designs that stabilized a desired nanoscale morphology. In RAPSIDY 2.0 we use a combination of molecular dynamics simulations and Bayesian optimization driven active learning to optimally query high-dimensional polymer design spaces and propose promising design candidates that simultaneously stabilize a selected nanoscale morphology and exhibit desired macroscale material properties. We utilize MD simulations with polymer chains preplaced into selected nanoscale morphologies and perform virtual experiments to determine the stability of the chosen polymer design within the target morphology and calculate the desired macroscale material properties (e.g., thermal conductivity). Our methodology directly addresses the unique challenge associated with copolymers, whose macroscale properties are a function of both their chain design and mesoscale morphology, which are coupled.