Data-efficient Machine Learning for Polymer Informatics

Xin-Yao Xu; Xiao Hu; Li-Quan Wang; Ying Jiang

doi:10.1007/s10118-025-3401-z

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Data-efficient Machine Learning for Polymer Informatics

REVIEW | Updated：2025-09-26

- Data-efficient Machine Learning for Polymer Informatics
- Data-efficient Machine Learning for Polymer Informatics
- 高分子科学（英文） 2025年43卷第10期页码：1707-1717
- Affiliations：
  
  a.School of Chemistry and Chemical Engineering, Shaoxing University, Shaoxing 312000, China
  b.Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
  c.Zhejiang Key Laboratory of Functional Ionic Membrane Materials and Technology for Hydrogen Production, Shaoxing 312000, China
- Author bio：
  
  lq_wang@ecust.edu.cn (L.Q.W.)
  yjiang@usx.edu.cn (Y.J.)
- Funds：
  
  This work was financially supported by the National Natural Science Foundation of China (No. 22473006) and the Central Government Guiding Local Science and Technology Development Fund (No. 2025ZY01029).;Li-Quan Wang received his Ph.D. degree from East China University of Science and Technology in 2011. He is currently a Professor in the School of Materials Science and Engineering, East China University of Science and Technology. His research interest is now focused on the design approach of thermosetting polymers.Ying Jiang received his Ph.D. in Chemistry from the University of Science and Technology of China (2006) and a Ph.D. in Physics from the University of Waterloo (2013). He conducted postdoctoral research at the Colorado State University and the University of Science and Technology of China (2006−2009). From 2014 to 2025, he served as Associate Professor and Later Professor at Beihang University. He is currently a Professor in the School of Chemistry and Chemical Engineering at Shaoxing University. His research focuses on theoretical investigations of surface/interfacial effects in polymeric materials, employing mesoscale simulations and machine learning to study equilibrium/non-equilibrium properties of polymer-interface systems and elucidate their physical mechanisms in modulating material functionality.
- DOI：10.1007/s10118-025-3401-z
  中图分类号：
- 收稿日期：2025-05-28，
  
  修回日期：2025-06-22，
  
  录用日期：2025-06-25，
  
  网络出版日期：2025-09-02，
  
  纸质出版日期：2025-10-05
- Accepted：
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Xu, X. Y.; Hu, X.; Wang, L. Q.; Jiang, Y. Data-efficient machine learning for polymer informatics. Chinese J. Polym. Sci. 2025, 43, 1707–1717

Xin-Yao Xu, Xiao Hu, Li-Quan Wang, et al. Data-efficient Machine Learning for Polymer Informatics[J]. Chinese journal of polymer science, 2025, 43(10): 1707-1717.
Xu, X. Y.; Hu, X.; Wang, L. Q.; Jiang, Y. Data-efficient machine learning for polymer informatics. Chinese J. Polym. Sci. 2025, 43, 1707–1717 DOI： 10.1007/s10118-025-3401-z.

Xin-Yao Xu, Xiao Hu, Li-Quan Wang, et al. Data-efficient Machine Learning for Polymer Informatics[J]. Chinese journal of polymer science, 2025, 43(10): 1707-1717. DOI： 10.1007/s10118-025-3401-z.

摘要

This review summarizes machine learning strategies tailored for polymers under small data regimes

highlighting key advances in data augmentation

domain-informed modeling

and efficient learning paradigms such as transfer and multitask learning. These approaches enhance predictive performance despite data scarcity.

Abstract

Polymer informatics faces challenges owing to data scarcity arising from complex chemistries

experimental limitations

and processing-dependent properties. This review presents the recent advances in data-efficient machine learning for polymers. First

data preparation techniques such as data augmentation and rational representation help expand the dataset size and develop useful features for learning. Second

modeling approaches

including classical algorithms and physics-informed methods

enhance the model robustness and reliability under limited data conditions. Third

learning strategies

such as transfer learning and active learning

aim to improve generalization and guide efficient data acquisition. This review concludes by outlining future opportunities in machine learning for small-data scenarios in polymers. This review is expected to serve as a useful tool for newcomers and offer deeper insights for experienced researchers in the field.

关键词

Keywords

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