Xu Xinyao, Zhao Wenlin, Wang Liquan, Lin Jiaping, Du Lei
Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology Shanghai 200237 China
Chem Sci. 2023 Sep 6;14(37):10203-10211. doi: 10.1039/d3sc03174h. eCollection 2023 Sep 27.
The traditional approach employed in copolymer compositional design, which relies on trial-and-error, faces low-efficiency and high-cost obstacles when attempting to simultaneously improve multiple conflicting properties. For example, designing co-cured polycyanurates that exhibit both moisture and thermal resistance, along with high modulus, is a long-term challenge because of the intrinsic trade-offs between these properties. In this work, to surmount these barriers, we developed a Bayesian optimization (BO)-guided method to expedite the discovery of co-cured polycyanurates exhibiting low water uptake, coupled with higher glass transition temperature and Young's modulus. By virtue of the knowledge of molecular simulations, benchmarking studies were carried out to develop an effective BO-guided method. Propelled by the developed method, several copolymers with improved comprehensive properties were obtained experimentally in a few iterations. This work provides guidance for efficiently designing other high-performance copolymers.
共聚物组成设计中采用的传统方法依赖于反复试验，在试图同时改善多个相互冲突的性能时面临效率低下和成本高昂的障碍。例如，设计兼具防潮性、耐热性和高模量的共固化聚氰尿酸酯是一项长期挑战，因为这些性能之间存在内在的权衡。在这项工作中，为了克服这些障碍，我们开发了一种贝叶斯优化（BO）引导的方法，以加速发现具有低吸水率、较高玻璃化转变温度和杨氏模量的共固化聚氰尿酸酯。借助分子模拟知识，开展了基准研究以开发有效的BO引导方法。在该方法的推动下，通过几次迭代实验获得了几种综合性能得到改善的共聚物。这项工作为高效设计其他高性能共聚物提供了指导。
