A prospective on machine learning challenges, progress, and potential in polymer science

Struble, Daniel C.; Lamb, Bradley G.; Ma, Boran

doi:10.1557/s43579-024-00587-8

Title: A prospective on machine learning challenges, progress, and potential in polymer science

Journal Article · 01 July 2024 · MRS communications

DOI: https://doi.org/10.1557/s43579-024-00587-8 · OSTI ID:2472974

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Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science and engineering every year. Polymer science is no different, though implementation of data-driven algorithms in this subfield has unique challenges barring widespread application of these techniques to the study of polymer systems. In this Prospective, we discuss several critical challenges to implementation of ML in polymer science, including polymer structure and representation, high-throughput techniques and limitations, and limited data availability. Promising studies targeting resolution of these issues are explored, and contemporary research demonstrating the potential of ML in polymer science despite existing obstacles are discussed. Finally, we present an outlook for ML in polymer science moving forward. Graphical Abstract

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Sponsoring Organization:: USDOE

Grant/Contract Number:: SC0024432

OSTI ID:: 2472974

Journal Information:: MRS communications, Journal Name: MRS communications Journal Issue: 5 Vol. 14; ISSN 2159-6867

Publisher:: Cambridge University Press (CUP)Copyright Statement

Country of Publication:: United States

Language:: English

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