Liao Vinson, Jayaraman Arthi
Department of Chemical and Biomolecular Engineering, University of Delaware, Colburn Lab, 150 Academy Street, Newark, Delaware 19716, United States.
Department of Materials Science and Engineering, University of Delaware, 201 DuPont Hall, Newark, Delaware 19716, United States.
JACS Au. 2025 Jun 5;5(6):2810-2824. doi: 10.1021/jacsau.5c00377. eCollection 2025 Jun 23.
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 to efficiently explore the high-dimensional polymer design space. Optimizing macroscale material properties for polymeric systems is even more challenging than inorganics and small molecules as these 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 (nm to microns) domain structures. In this work, we present an efficient high-throughput in-silico based framework to effectively design high-performance polymers (blends, copolymers) with desired multiscale 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 (MD) 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 (e.g., tensile strength, thermal conductivity). 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. 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. We showcase the efficacy of our methodology in engineering high-performance blends of block copolymers that exhibit (1) high thermal conductivity and (2) high tensile strength. We also discuss the impact of our work in accelerating the design of novel polymeric materials for targeted applications.
由于存在大量可能的聚合物设计变量，合理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。为了加速这一设计过程，迫切需要开发新工具来辅助逆向设计过程，并有效探索高维聚合物设计空间。与无机材料和小分子相比，优化聚合物体系的宏观材料性能更具挑战性，因为这些性能由多种长度尺度上的特征决定，从所选的单体化学结构到链级设计，再到更大尺度（纳米到微米）的域结构。在这项工作中，我们提出了一个基于高效高通量计算机模拟的框架，以有效地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（共混物、共聚物），我们将其称为RAPSIDY 2.0——聚合物结构快速分析与逆向设计策略2.0。RAPSIDY的这个新版本建立在我们之前的工作RAPSIDY 1.0的基础上，RAPSIDY 1.0纯粹专注于识别能够稳定所需纳米级形态的聚合物设计。在RAPSIDY 2.0中，我们结合分子动力学（MD）模拟和贝叶斯优化驱动的主动学习，以最佳方式查询高维聚合物设计空间，并提出有前景的设计候选方案，这些方案能同时稳定选定的纳米级形态并展现所需的宏观材料性能（如拉伸强度、热导率）。我们利用预先将聚合物链置于选定纳米级形态中的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。我们的方法直接应对了与共聚物相关的独特挑战，共聚物的宏观性能是其链设计和中尺度形态的函数，且二者相互耦合。我们展示了我们的方法在设计具有（1）高导热性和（2）高拉伸强度特性的嵌段共聚物高性能共混物方面的有效性。我们还讨论了我们的工作对加速针对特定应用的新型聚合物材料设计的影响。
