Processing-integrated Machine Learning Models for Predicting and Optimizing Mechanical Properties of Polyimides

Wei-Long Hu; Hao-Ke Qiu; En-Zhe Jing; Wan-Chen Zhao; Hai-Yang Huo; Zhao-Yan Sun

doi:10.1007/s10118-026-3643-4

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Processing-integrated Machine Learning Models for Predicting and Optimizing Mechanical Properties of Polyimides

RESEARCH ARTICLE | Updated：2026-04-16

- Processing-integrated Machine Learning Models for Predicting and Optimizing Mechanical Properties of Polyimides
- Processing-integrated Machine Learning Models for Predicting and Optimizing Mechanical Properties of Polyimides
- Chinese Journal of Polymer Science 2026年44卷页码：1-10
- Affiliations：
  
  a.School of Applied Chemistry and Engineering, University of Science and Technology of China, Hefei 230026, China
  b.State Key Laboratory of Polymer Physics and Chemistry & Key Laboratory of Polymer Science and Technology, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun 130022, China
  c.Jilin Provincial International Cooperation Key Laboratory for Polymer Processing Physics, Changchun 130022, China
- Author bio：
  
  zysun@ciac.ac.cn
- Funds：
  
  This work was financially supported by the National Natural Science Foundation of China (No. 52293471) and the Jilin Provincial Science and Technology Development Program (Nos. SKL202602006JC, SKL202502004JC). We are also grateful for the essential support of Jilin Provincial International Cooperation Key Laboratory for Polymer Processing Physics and the Network and Computing Center, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences.;The authors declare no interest conflict.Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-026-3643-4.;The data that support the findings of this study are available from the corresponding author upon reasonable request. The author’s contact information: zysun@ciac.ac.cn (Z.Y.S.)
- DOI：10.1007/s10118-026-3643-4
  中图分类号：
- 收稿：2026-02-03，
  
  修回：2026-02-20，
  
  录用：2026-03-05，
  
  网络首发：2026-04-16，
  
  纸质出版：2026-03
- Accepted：
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Hu, W. L.; Qiu, H. K.; Jing, E. Z.; Zhao, W. C.; Huo, H. Y.; Sun, Z. Y. Processing-integrated machine learning models for predicting and optimizing mechanical properties of polyimides. Chinese J. Polym. Sci. https://doi.org/10.1007/s10118-026-3643-4

Wei-Long Hu, Hao-Ke Qiu, En-Zhe Jing, et al. Processing-integrated Machine Learning Models for Predicting and Optimizing Mechanical Properties of Polyimides[J/OL]. Chinese Journal of Polymer Science, 2026, 441-10.
Hu, W. L.; Qiu, H. K.; Jing, E. Z.; Zhao, W. C.; Huo, H. Y.; Sun, Z. Y. Processing-integrated machine learning models for predicting and optimizing mechanical properties of polyimides. Chinese J. Polym. Sci. https://doi.org/10.1007/s10118-026-3643-4 DOI：

Wei-Long Hu, Hao-Ke Qiu, En-Zhe Jing, et al. Processing-integrated Machine Learning Models for Predicting and Optimizing Mechanical Properties of Polyimides[J/OL]. Chinese Journal of Polymer Science, 2026, 441-10. DOI： 10.1007/s10118-026-3643-4.

摘要

Abstract

Polyimides (PIs) are widely used in industry owing to their excellent mechanical properties and thermomechanical stability

which depend not only on molecular structure but also on processing conditions. In this study

we present a machine-learning-based strategy for predicting and optimizing the mechanical properties of PI materials by explicitly incorporating processing information into predictive models. Three machine learning models were developed to evaluate PI structures together with thermal imidization parameters

with the aim of improving the prediction accuracy of mechanical properties and enhancing the interpretability of structure-processing-property relationships. By analyzing structural and processing descriptors

key factors influencing tensile strength

Young's modulus

and elongation at break were identified. The results indicate that

in addition to molecular descriptors

processing-related features plays a substantial role on multiple mechanical properties. Based on the trained models

we further developed an automated tool that accepts a SMILES representation of a PI structure as input and outputs the predicted mechanical properties along with the corresponding processing conditions associated with optimal performance. This work provides a data-driven framework for guiding PI material design and process optimization

and offers a practical basis for future experimental validation. Our proposed approach is readily extendable to other polymer systems and polymer composites where processing plays an important role in determining mechanical behavior.

关键词

Keywords

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