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<p>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 <i>via</i> 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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • 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

摘要

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.