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

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.

Automated summary

This review introduces recent developments in data-driven approaches for structure-property relationships in polymer science. It emphasizes the significant challenge of understanding the unique structures and properties generated by long molecular chains, and summarizes related research reports and methods.