Machine-learning-assisted Materials Genome Approach for Designing High-performance Thermosetting Polyimides

Wan-Xun Feng; Song-Qi Zhang; Yin-Yi Xu; Xiang-Fei Ye; Xin-Yao Xu; Li-Quan Wang; Jia-Ping Lin; Chun-Hua Cai; Lei Du

doi:10.1007/s10118-025-3403-x

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Machine-learning-assisted Materials Genome Approach for Designing High-performance Thermosetting Polyimides

RESEARCH ARTICLE | Updated：2025-09-26

- Machine-learning-assisted Materials Genome Approach for Designing High-performance Thermosetting Polyimides
- Chinese Journal of Polymer Science Vol. 43, Issue 10, Pages: 1718-1729(2025)
- Affiliations：
  
  Shanghai Key Laboratory of Advanced Polymeric Materials, Key Laboratory for Ultrafine Materials of Ministry of Education, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Materials Science and Engineering, East China University of Science and Technology, Shanghai 200237, China
- Author bio：
  
  lq_wang@ecust.edu.cn (L.Q.W.)
  jlin@ecust.edu.cn (J. P.L.)
- Funds：
- DOI：10.1007/s10118-025-3403-x
  CLC：
- Received：29 April 2025，
  
  Revised：2025-06-14，
  
  Accepted：25 June 2025，
  
  Online First：02 September 2025，
  
  Published：05 October 2025
- Accepted：
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Feng, W. X.; Zhang, S. Q.; Xu, Y. Y.; Ye, X. F.; Xu, X. Y.; Wang, L. Q.; Lin, J. P.; Cai, C. H.; Du, L. Machine-learning-assisted materials genome approach for designing high-performance thermosetting polyimides. Chinese J. Polym. Sci. 2025, 43, 1718–1729

Wan-Xun Feng, Song-Qi Zhang, Yin-Yi Xu, et al. Machine-learning-assisted Materials Genome Approach for Designing High-performance Thermosetting Polyimides[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1718-1729.
Feng, W. X.; Zhang, S. Q.; Xu, Y. Y.; Ye, X. F.; Xu, X. Y.; Wang, L. Q.; Lin, J. P.; Cai, C. H.; Du, L. Machine-learning-assisted materials genome approach for designing high-performance thermosetting polyimides. Chinese J. Polym. Sci. 2025, 43, 1718–1729 DOI： 10.1007/s10118-025-3403-x.

Wan-Xun Feng, Song-Qi Zhang, Yin-Yi Xu, et al. Machine-learning-assisted Materials Genome Approach for Designing High-performance Thermosetting Polyimides[J]. Chinese Journal of Polymer Science, 2025, 43(10): 1718-1729. DOI： 10.1007/s10118-025-3403-x.

摘要

Machine learning models were developed to predict Young's modulus

tensile strength

and elongation at break

exploring the chemical space of thousands of polyimide candidates and offering a cost-effective approach for designing high-performance films.

Abstract

Enhancing the mechanical properties is crucial for polyimide films

but the mechanical properties (Young's modulus

tensile strength

and elongation at break) mutually constrain each other

complicating simultaneous enhancement

via

traditional trial-and-error methods. In this work

we proposed a materials genome approach to design and screen phenylethynyl-terminated polyimides for films with enhanced mechanical properties. We first established machine learning models to predict Young's modulus

tensile strength

and elongation at break to explore the chemical space containing thousands of candidate structures. The accuracies of the machine learning models were verified by molecular dynamics simulations on screened polyimides and experimental testing on three representative polyimide films. The performance advantages of the best-selected polyimides were analyzed by comparing well-known polyimides based on molecular dynamics simulations

and the structural rationale was revealed by "gene" analysis and feature importance evaluation. This work provides a cost-effective strategy for designing polyimide films with enhanced mechanical properties.

关键词

Keywords

references

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