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Applications of Machine Learning in Polymer Materials: Property Prediction, Material Design, and Systematic Processes

Hongtao Guo¹, Shuai Li², Shu Li^1,*

1 Key Laboratory of Engineering Dielectric and Applications (Ministry of Education), School of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin, China
2 School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin, China

* Corresponding Author: Shu Li. Email:

(This article belongs to the Special Issue: Machine Learning Methods in Materials Science)

Computers, Materials & Continua 2026, 87(3), 2 https://doi.org/10.32604/cmc.2026.076492

Received 21 November 2025; Accepted 27 January 2026; Issue published 09 April 2026

Abstract

This paper reviews the research progress and application prospects of machine learning technologies in the field of polymer materials. Currently, machine learning methods are developing rapidly in polymer material research; although they have significantly accelerated material prediction and design, their complexity has also caused difficulties in understanding and application for researchers in traditional fields. In response to the above issues, this paper first analyzes the inherent challenges in the research and development of polymer materials, including structural complexity and the limitations of traditional trial-and-error methods. To address these problems, it focuses on introducing key basic technologies such as molecular descriptors and feature representation, data standardization and cleaning, and records a number of high-quality polymer databases. Subsequently, it elaborates on the key role of machine learning in polymer property prediction and material design, covering the specific applications of algorithms such as traditional machine learning, deep learning, and transfer learning; further, it deeply expounds on data-driven design strategies, such as reverse design, high-throughput virtual screening, and multi-objective optimization. The paper also systematically introduces the complete process of constructing high-reliability machine learning models and summarizes effective experimental verification, model evaluation, and optimization methods. Finally, it summarizes the current technical challenges in research, such as data quality and model generalization ability, and looks forward to future development trends including multi-scale modeling, physics—informed machine learning, standardized data sharing, and interpretable machine learning.

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