Issue 29, 2023

A review on the application of molecular descriptors and machine learning in polymer design

Abstract

Polymers are an important class of materials with vast arrays of physical and chemical properties and have been widely used in many applications and industrial products. Although there have been many successful polymer design studies, the pace of materials discovery research can be accelerated to meet the high demand for new, functional materials. With the advanced development of artificial intelligence, the use of machine learning has shown great potential in data-driven design and the discovery of polymers to date. Several polymer datasets have been compiled, allowing robust machine learning models to be trained and provide accurate predictions of various polymer properties. Such models are useful for screening promising candidate polymers with high-performing properties prior to lab synthesis. In this review, we focus on the most critical components of polymer design using molecular descriptors and machine learning algorithms. A summary of existing polymer databases is provided, and the different categories of polymer descriptors are discussed in detail. The application of these descriptors in machine learning studies of polymer design is critically reviewed, leading to a discussion of the challenges, opportunities, and future perspectives for polymer research using these advanced computational tools.

Graphical abstract: A review on the application of molecular descriptors and machine learning in polymer design

Article information

Article type
Review Article
Submitted
13 Apr 2023
Accepted
27 Jun 2023
First published
11 Jul 2023

Polym. Chem., 2023,14, 3325-3346

Author version available

A review on the application of molecular descriptors and machine learning in polymer design

Y. Zhao, R. J. Mulder, S. Houshyar and T. C. Le, Polym. Chem., 2023, 14, 3325 DOI: 10.1039/D3PY00395G

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements