Data-driven approaches for structure-property relationships in polymer science for prediction and understanding

被引：33

作者：

Amamoto, Yoshifumi ^{[1
]}

机构：

[1] Kyushu Univ, Inst Mat Chem & Engn, Nishi Ku, 744 Motooka, Fukuoka 8190395, Japan

来源：

POLYMER JOURNAL | 2022年 / 54卷 / 08期

关键词：

BLOCK-COPOLYMERS; INVERSE DESIGN; MACHINE; EXPLORATION; ELASTOMERS; DISCOVERY;

D O I：

10.1038/s41428-022-00648-6

中图分类号：

O63 [高分子化学（高聚物）];

学科分类号：

070305 ; 080501 ; 081704 ;

摘要：

In this review, recent developments in data-driven approaches for structure-property relationships in polymer science are introduced. Understanding the structure-property relationship in polymeric materials is a significant challenge. This is because long molecular chains generate unique structures and properties over a wide range of spatial and temporal scales, which are often difficult to address using theoretical models or single simulation/measurement techniques. Recently, the data-driven modeling of structure-property relationships based on statistical/informatics methods has been employed in polymer science to obtain the desired properties and understand the mechanisms. This review summarizes the reports from this domain in the previous three years. A concept and some methods in data-driven science are first explained to readers unfamiliar with this area. Additionally, various examples, such as the description of a single chain, phase separations, network polymers, and crystalline polymers, are introduced. A topic for dealing with chemically specified coarse-grained simulations is also included. Finally, future perspectives in this area are presented.

引用

页码：957 / 967

页数：11

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