期刊
ACTA POLYMERICA SINICA
卷 53, 期 6, 页码 592-607出版社
SCIENCE PRESS
DOI: 10.11777/j.issn1000-3304.2021.21404
关键词
Material genome approach; Polymer; Machine learning; Simulation
The materials genome approach (MGA) is capable of accelerating polymer research and development, but the complex structural features of polymers make it challenging to establish structure-property relationships. Recent studies have focused on rational design of advanced polymers using MGA.
The materials genome approach (MGA), which can accelerate the research and development of new materials via combining computer technology, database technology, and experiments, has attracted considerable attention from academia and industry. However, establishing the structure-property relationship of polymer is more complicated than those of metal and inorganic materials because of the complex structural features such as chain architecture, chain configuration, chain conformation, and chain aggregation. The difficulty in building the structure-property relationship has hindered the development of MGA in polymers. Recently, there have been increasing studies on the rational design of advanced polymers by MGA. This review summarizes research progresses on the MGA of polymer, including establishing structure-property relationships that can predict polymer properties, exploring the vast chemical space of polymers, and rationally designing polymer structures. Calculating the key features that correlate with the desired properties from data mining is one of the ways to screen promising polymers. Alternatively, machine learning can construct structure-property relationships automatically based on databases. The model based on machine learning can apply to the forward and inverse design of advanced polymers. These two prevalent methods are presented. The review systematically introduces the methods of data mining or model construction and the ideas of screening different types of polymers by using these models and focuses on the ideas behind method construction and material screening and the solutions to various problems. In addition, the challenges faced by the development of polymer MGA are also outlined. To push forward the research on polymer MGA, we suggest paying more attention to introducing more efficient machine learning technology, establishing more comprehensive polymer databases, and developing high-throughput experimental technology in the future. [GRAPHICS]
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