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

Your Location：

Home >

Browse articles >

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
- Machine-learning-assisted Materials Genome Approach for Designing High-performance Thermosetting Polyimides
- 高分子科学（英文） 2025年43卷第10期页码：1718-1729
- 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：
  
  This work was financially supported by the National Key R&D Program of China (No. 2022YFB3707302) and the National Natural Science Foundation of China (Nos. 52394271 and 52394270).;Electronic supplementary information (ESI) is available free of charge in the online version of this article at http://doi.org/10.1007/s10118-025-3403-x.
- DOI：10.1007/s10118-025-3403-x
  中图分类号：
- 收稿：2025-04-29，
  
  修回：2025-06-14，
  
  录用：2025-06-25，
  
  网络首发：2025-09-02，
  
  纸质出版：2025-10-05
- Accepted：
Scan QR Code
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

Liu, X. J.; Zheng, M. S.; Chen, G.; Dang, Z. M.; Zha, J. W. High-temperature polyimide dielectric materials for energy storage: theory, design, preparation and properties. Energy Environ. Sci. 2022 , 15 , 56−81..

[Zi, Y.; Wu, G.; Pei, D.; Guo, S.; Qi, S.; Tian, G.; Wu, D., Decreasing the CTE of thermoplastic polyimide by blending with a thermosetting system. Chinese J. Polym. Sci . 2023 , 41 , 956−961..

Yi, C.; Li, W.; Shi, S.; He, K.; Ma, P.; Chen, M.; Yang, C. High-temperature-resistant and colorless polyimide: Preparations, properties, and applications. Solar Energy 2020 , 195 , 340−354..

Liaw, D. J.; Wang, K. L.; Huang, Y. C.; Lee, K. R.; Lai, J. Y.; Ha, C. S. Advanced polyimide materials: syntheses, physical properties and applications. Prog. Polym. Sci. 2012 , 37 , 907−974..

Hung, Y. T.; Chen, C. K.; Lin, Y. C.; Yu, Y. Y.; Chen, W. C. Dimensionally thermally stable biomass-based polyimides for flexible electronic applications. Polym. J. 2022 , 54 , 1489−1499..

Gao, G.; Zhang, S.; Wang, L.; Lin, J.; Qi, H.; Zhu, J.; Du, L.; Chu, M. Developing highly tough, heat-resistant blend thermosets based on silicon-containing arylacetylene: a material genome approach. ACS Appl. Mater. Interfaces 2020 , 12 , 27587−27597..

[Tenney, D. R. Structural framework for flight II: NASA's role in development of advanced composite materials for aircraft and space structures . National Aeronautics and Space Administration, Langley Research Center: 2019.

Liu, C.; Zhao, X.; Yu, X.; Wang, W.; Jia, H.; Li, Y.; Zhou, H.; Chen, C. A study of the thermal cure of new trifunctional phenylethynyl terminated imide oligomers with reduced cure temperatures. Polym. Degrad. Stabil. 2013 , 98 , 230−240..

Zhou, X.; Wan, L.; Hu, Y.; E, Y.; Huang, F.; Du, L. Synthesis and characterization of novel polytriazoleimides by CuAAC step-growth polymerization. Polym. J. 2010 , 42 , 216−222..

Gong, C.; Luo, Q.; Li, Y.; Giotto, M.; Cipollini, N. E.; Yang, Z.; Weiss, R. A.; Scola, D. A. Free radical initiated low temperature crosslinking of phenylethynyl (PE) end-capped oligomides. J. Polym. Sci., Part A: Polym. Chem. 2010 , 48 , 3950−3963..

Wang, Y.; Liu, Y.; Song, S.; Yang, Z.; Qi, X.; Wang, K.; Liu, Y.; Zhang, Q.; Tian, Y. Accelerating the discovery of insensitive high-energy-density materials by a materials genome approach. Nat. Commun. 2018 , 9 , 2444..

Gautier, R.; Zhang, X.; Hu, L.; Yu, L.; Lin, Y.; Sunde, T. O. L.; Chon, D.; Poeppelmeier, K. R.; Zunger, A. Prediction and accelerated laboratory discovery of previ ously unknown 18-electron ABX compounds. Nat. Chem. 2015 , 7 , 308−316..

Li, X.; Li, B.; Yang, Z.; Chen, Z.; Gao, W.; Jiang, Q. A transferable machine-learning scheme from pure metals to alloys for predicting adsorption energies. J. Mater. Chem. A 2022 , 10 , 872−880..

Boyd, P. G.; Lee, Y.; Smit, B. Computational development of the nanoporous materials genome. Nat. Rev. Mater. 2017 , 2 , 17037..

Zhu, J.; Chu, M.; Chen, Z.; Wang, L.; Lin, J.; Du, L. Rational design of heat-resistant polymers with low curing energies by a materials genome approach. Chem. Mater. 2020 , 32 , 4527−4535..

Ma, R.; Baldwin, A. F.; Wang, C.; Offenbach, I.; Cakmak, M.; Ramprasad, R.; Sotzing, G. A. Rationally designed polyimides for high-energy density capacitor applications. ACS Appl. Mater. Interfaces 2014 , 6 , 10445−10451..

Li, Y.; Jiang, Y.; Wang, L.; Li, J. Data and Machine Learning in Polymer Science. Chinese J. Polym. Sci. 2023 , 41 , 1371−1376..

Ding, F.; Liu, L.; Liu, T.; Li, Y.; Li, J.; Sun, Z. Predicting the mechanical properties of polyurethane elastomers using machine learning. Chinese J. Polym. Sci. 2023 , 41 , 422−431..

Hu, Y.; Zhao, W.; Wang, L.; Lin, J.; Du, L. Machine-learning-assisted design of highly tough thermosetting polymers. ACS Appl. Mater. Interfaces 2022 , 14 , 55004−55016.

Shahriari, B.; Swersky, K.; Wang, Z. Y.; Adams, R. P.; de Freitas, N. Taking the human out of the loop: A review of Bayesian optimization. Proc. IEEE 2016 , 104 , 148−175..

Du, Shi.; Zhang, S.; Wang, L.; Lin, J.; Du, L. Polymer genome approach: A new method for research and development of polymers. Acta Polymerica Sinica (in Chinese) 2022 , 53 , 592−607..

[AI plus Polymers Database. https://polymergenome.ecust.edu.cn (accessed 3/16, 2025).

Tao, L.; Chen, G.; Li, Y. Machine learning discovery of high-temperature polymers. Patterns 2021 , 2 , 100225..

Abbott, L. J.; Hart, K. E.; Colina, C. M. Polymatic: a generalized simulated polymerization algorithm for amorphous polymers. Theor. Chem. Acc. 2013 , 132 , 1334..

Sun, H. COMPASS: An ab initio force-field optimized for condensed-phase applications-overview with details on alkane and benzene compounds. J. Phys. Chem. B 1998 , 102 , 7338−7364..

Shokuhfar, A.; Arab, B. The effect of cross linking density on the mechanical properties and structure of the epoxy polymers: Molecular dynamics simulation. J. Mol. Model. 2013 , 19 , 3719−3731..

Mannodi-Kanakkithodi, A.; Chandrasekaran, A.; Kim, C.; Huan, T. D.; Pilania, G.; Botu, V.; Ramprasad, R. Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond. Mater. Today 2018 , 21 , 785−796..

[PubChem Database. https://pubchem.ncbi.nlm.nih.gov (accessed 9/16).

[Liu, Y.; Wang, Z.; Yang, H.; Gao, L.; Li, G.; Ding, M. 3-Phenylethynyl phthalimide end-capped imide oligomers and the cured polymers. J. Polym. Sci., Part A: Polym. Chem . 2008 , 46 , 4227−4235..

Guo, K.; Yang, Z. Z.; Yu, C. H.; Buehler, M. J. Artificial intelligence and machine learning in design of mechanical materials. Mater. Horiz. 2021 , 8 , 1153−1172..

Gu, G. X.; Chen, C. T.; Buehler, M. J. De novo composite design based on machine learning algorithm. Extreme Mech. Lett. 2018 , 18 , 19−28..

Bhowmik, R.; Sihn, S.; Pachter, R.; Vernon, J. P. Prediction of the specific heat of polymers from experimental data and machine learning methods. Polymer 2021 , 220 , 123558..

[Bicerano, J. Prediction of Polymer Properties . CRC Press: Boca Raton, 2002 , p. 1−15..

[Rasmussen, C. E.; Williams, C. K. I. Gaussian Processes for Machine Learning . MIT Press: Cambridge, 2006 , p. 177..

Galant, O.; Bae, S.; Silberstein, M. N.; Diesendruck, C. E. Highly stretchable polymers: mechanical properties improvement by balancing intra- and intermolecular interactions. Adv. Funct. Mater. 2020 , 30 , 1901806..

Sirk, T. W.; Khare, K. S.; Karim, M.; Lenhart, J. L.; Andzelm, J. W.; McKenna, G. B.; Khare, R. High strain rate mechanical properties of a crosslinked epoxy across the glass transition. Polymer 2013 , 54 , 7048−7057..

Chowdhury, S. C.; Elder, R. M.; Sirk, T. W.; Gillespie, J. W. Epoxy resin thermo-mechanics and failure modes: Effects of cure and cross-linker length. Comp. B 2020 , 186 , 107814..

Brown, D.; Clarke, J. H. R. Molecular dynamics simulation of an amorphous polymer under tension. 1. Phenomenology. Macromolecules 1991 , 24 , 2075−2082..

Hergenrother, P. M.; Smith, J. G. Chemistry and propert ies of imide oligomers end-capped with phenylethynylphthalic anhydrides. Polymer 1994 , 35 , 4857−4864..

Fang, X.; Rogers, D. F.; Scola, D. A.; Stevens, M. P. A study of the thermal cure of a phenylethynyl-terminated imide model compound and a phenylethynyl-terminated imide oligomer (PETI-5). J. Polym. Sci., Part A: Polym. Chem. 1998 , 36 , 461−470..

Chen, Z.; Wang, L.; Lin, J.; Du, L. Theoretical study on thermal curing mechanism of arylethynyl-containing resins. Phys. Chem. Chem. Phys. 2020 , 22 , 6468−6477..

Bryant, R.G.; Jensen, B. J.; Hergenrother, P. M. Chemistry and properties of a phenylethynyl-terminated polyimide. J. Appl. Polym. Sci. 1996 , 59 , 1249−1254..

[Wang, W.; Chen, G.; Fang, X. 1,4-Bis(2,3-dicarboxyl-phenoxy)benzene dianhydride-based phenylethynyl terminated thermoset oligoimides for resin transfer molding applications. J. Appl. Polym. Sci . 2019 , 136 , 47967..

Zdeborová, L. Understanding deep learning is also a j ob for physicists. Nat. Phys. 2020 , 16 , 602−604..

Dombi, G. W.; Nandi, P.; Saxe, J. M.; Ledgerwood, A. M.; Lucas, C. E. Prediction of rib fracture injury outcome by an artificial neural network. J. Trauma Acute Care Surg. 1995 , 39 , 915−921..

Stanton, D. T. Evaluation and use of BCUT descriptors in QSAR and QSPR Studies. J. Chem. Inf. Comput. Sci. 1999 , 39 , 11−20..

Hall, L. H.; Mohney, B.; Kier, L. B. The electrotopological state: An atom index for QSAR. Quant. Struct. Act. Relat. 1991 , 10 , 43−51..

浏览量

725

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

Crosslinked Colorless Polyimide Films via Oxazole Groups as Crosslinking Agent: Preparation and Properties

Molecularly Engineered Polyimide Films with Switchable Cavitation for Bidirectional Passive Thermal Regulation

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

Tunable Circularly Polarized Luminescence of Helical Poly(phenylacetylene)s via Side-chain-to-Backbone Förster Resonance Energy Transfer

Optically Active Helical Poly(phenyl isocyanide)s Bearing Achiral Benzanilide Pendants: from Controlled Synthesis to Enantioseparation Application