Physics Department, Lomonosov Moscow State University, Moscow 119991, Russian Federation.
Semenov Federal Research Center for Chemical Physics, Moscow 119991, Russian Federation.
J Phys Chem B. 2023 Feb 23;127(7):1479-1489. doi: 10.1021/acs.jpcb.2c07689. Epub 2023 Feb 15.
The copolymer sequence can be considered as a new tool to shape the resulting system properties on demand. This perspective is devoted to copolymers with "partially segregated" (or nonblocky) sequences. Such copolymers include gradient copolymers and copolymers with random sequences as well as copolymers with precisely controlled sequences. We overview recent developments in the synthesis of these systems as well as new findings regarding their properties, in particular, self-assembly in solutions and in melts. An emphasis is put on how the microscopic behavior of polymer chains is influenced by the chain sequences. In addition to that, a novel class of approaches allowing one to efficiently tackle the problem of copolymer chain sequence design─data driven methods (artificial intelligence and machine learning)─is discussed.
共聚物序列可以被视为一种按需塑造所得系统性质的新工具。本视角专注于具有“部分分离”（或非嵌段）序列的共聚物。此类共聚物包括梯度共聚物和具有无规序列的共聚物以及具有精确控制序列的共聚物。我们综述了这些体系的合成方面的最新进展以及关于其性质的新发现，特别是在溶液和熔体中的自组装。重点在于聚合物链的微观行为如何受到链序列的影响。除此之外，还讨论了一类新的方法，这些方法可以有效地解决共聚物链序列设计问题——数据驱动方法（人工智能和机器学习）。
