Machine Learning in Polymer Research

Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into...

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Bibliographic Details
Published in:Advanced materials (Weinheim) Vol. 37; no. 11; pp. e2413695 - n/a
Main Authors: Ge, Wei, De Silva, Ramindu, Fan, Yanan, Sisson, Scott A., Stenzel, Martina H.
Format: Journal Article
Language:English
Published: Germany Wiley Subscription Services, Inc 01.03.2025
John Wiley and Sons Inc
Subjects:
chemical descriptors
Chemical synthesis
Chemists
Datasets
FAIR data
Machine learning
Mathematicians
Molecular weight distribution
Polymer chemistry
Polymers
Review
chemical descriptors
polymers
machine learning
FAIR data
ISSN:0935-9648, 1521-4095, 1521-4095
Online Access:Get full text
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Summary:Machine learning is increasingly being applied in polymer chemistry to link chemical structures to macroscopic properties of polymers and to identify chemical patterns in the polymer structures that help improve specific properties. To facilitate this, a chemical dataset needs to be translated into machine readable descriptors. However, limited and inadequately curated datasets, broad molecular weight distributions, and irregular polymer configurations pose significant challenges. Most off the shelf mathematical models often need refinement for specific applications. Addressing these challenges demand a close collaboration between chemists and mathematicians as chemists must formulate research questions in mathematical terms while mathematicians are required to refine models for specific applications. This review unites both disciplines to address dataset curation hurdles and highlight advances in polymer synthesis and modeling that enhance data availability. It then surveys ML approaches used to predict solid‐state properties, solution behavior, composite performance, and emerging applications such as drug delivery and the polymer–biology interface. A perspective of the field is concluded and the importance of FAIR (findability, accessibility, interoperability, and reusability) data and the integration of polymer theory and data are discussed, and the thoughts on the machine–human interface are shared. Artificial intelligence (AI) has permeated every aspect of science, including polymer research. Researchers from both fields need to collaborate to understand the challenges and opportunities of each domain. This review is therefore written by mathematicians and polymer chemists to highlight the key research questions polymer chemists aim to address and how machine learning can assist in answering them.
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ISSN:0935-9648
1521-4095
1521-4095
DOI:10.1002/adma.202413695