Ma Longyu, Li Wenjing, Yuan Jian, Zhu Jian, Wu Yan, He Hanliang, Pan Xiangqiang
State and Local Joint Engineering Laboratory for Novel Functional Polymeric Materials, Jiangsu Key Laboratory of Advanced Functional Polymer Design and Application, College of Chemistry, Chemical Engineering and Materials Science, Soochow University, Suzhou, China.
College of Life Science, Mudanjiang Medical University, Mudanjiang, China.
Macromol Rapid Commun. 2025 Jul 7:e00361. doi: 10.1002/marc.202500361.
The traditional research paradigm for polymer materials relies heavily on time-consuming and inefficient trial-and-error methods, which are no longer sufficient to meet the demands of modern research and development. With the rapid advancement of big data and artificial intelligence technologies, machine learning has emerged as a powerful data analysis tool, revolutionizing polymer material research and development. This paper provides an overview of machine learning techniques, summarizes common machine learning algorithms, and reviews recent progress in machine learning-assisted polymer material design and development. Key areas include polymer sequence design, material property prediction, classification and identification, and applications leveraging computer vision technologies. Furthermore, this study discusses several critical challenges currently faced by the field and offers perspectives on future directions .
传统的高分子材料研究范式严重依赖耗时且低效的试错方法，已不足以满足现代研发的需求。随着大数据和人工智能技术的迅速发展，机器学习已成为一种强大的数据分析工具，彻底改变了高分子材料的研发。本文概述了机器学习技术，总结了常见的机器学习算法，并回顾了机器学习辅助高分子材料设计与开发的最新进展。关键领域包括聚合物序列设计、材料性能预测、分类与识别以及利用计算机视觉技术的应用。此外，本研究讨论了该领域目前面临的几个关键挑战，并对未来方向提出了展望。
