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：2025-09-03

- Data-efficient Machine Learning for Polymer Informatics
- Chinese Journal of Polymer Science Vol. 43, Pages: 1-11(2025)
- 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：
- DOI：10.1007/s10118-025-3401-z
  CLC：
- Received：28 May 2025，
  
  Revised：22 June 2025，
  
  Accepted：25 June 2025，
  
  Published Online：02 September 2025，
  
  Published：2025-07
- Accepted：
Scan QR Code
Xu, X. Y.; Hu, X.; Wang, L. Q.; Jiang, Y. Data-efficient machine learning for polymer informatics. Chinese J. Polym. Sci. https://doi.org/10.1007/s10118-025-3401-z

Xin-Yao Xu, Xiao Hu, Li-Quan Wang, et al. Data-efficient Machine Learning for Polymer Informatics[J/OL]. Chinese journal of polymer science, 2025, 431-11.
Xu, X. Y.; Hu, X.; Wang, L. Q.; Jiang, Y. Data-efficient machine learning for polymer informatics. Chinese J. Polym. Sci. https://doi.org/10.1007/s10118-025-3401-z DOI：

Xin-Yao Xu, Xiao Hu, Li-Quan Wang, et al. Data-efficient Machine Learning for Polymer Informatics[J/OL]. Chinese journal of polymer science, 2025, 431-11. DOI： 10.1007/s10118-025-3401-z.

摘要

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

references

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