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

Polymer Journal volume 54, pages 957–967 (2022)Cite this article

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Abstract

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.

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Acknowledgements

This work was supported by the JSPS Grant-in-Aid for Scientific Research on Innovative Areas, Discrete Geometric Analysis for Materials Design: 20H04644, by the Grant-in-Aid for Scientific Research (B): 20H02800, and by Early-Career Scientists: 18K14273 from JSPS.

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    Yoshifumi Amamoto

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Amamoto, Y. Data-driven approaches for structure-property relationships in polymer science for prediction and understanding. Polym J 54, 957–967 (2022). https://doi.org/10.1038/s41428-022-00648-6

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