Issue 33, 2021

Data-driven algorithms for inverse design of polymers

Abstract

The ever-increasing demand for novel polymers with superior properties requires a deeper understanding and exploration of the chemical space. Recently, data-driven approaches to explore the chemical space for polymer design have emerged. Among them, inverse design strategies for designing polymers with specific properties have evolved to be a significant materials informatics platform by learning hidden knowledge from materials data as well as smartly navigating the chemical space in an optimized way. In this review, we first summarize the progress in the representation of polymers, a prerequisite step for the inverse design of polymers. Then, we systematically introduce three data-driven strategies implemented for the inverse design of polymers, i.e., high-throughput virtual screening, global optimization, and generative models. Finally, we discuss the challenges and opportunities of the data-driven strategies as well as optimization algorithms employed in the inverse design of polymers.

Graphical abstract: Data-driven algorithms for inverse design of polymers

Article information

Article type
Review Article
Submitted
16 svi 2021
Accepted
12 srp 2021
First published
13 srp 2021

Soft Matter, 2021,17, 7607-7622

Author version available

Data-driven algorithms for inverse design of polymers

K. Sattari, Y. Xie and J. Lin, Soft Matter, 2021, 17, 7607 DOI: 10.1039/D1SM00725D

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