Polyimide films are essential in aerospace, flexible electronics, and micro-display technologies for their thermal stability and insulation. However, mechanical optimization remains elusive: high modulus often reduces toughness, and improving one property tends to compromise another. Traditional trial-and-error synthesis is slow, costly, and limited in exploring complex molecular spaces. The rise of materials-genome approaches—integrating computation, experiment, and AI—offers a solution by learning structure–property relationships directly from data. Yet, most prior models addressed single properties or small datasets. Due to these challenges, a systematic, high-throughput strategy is urgently needed to design polyimides with superior, well-balanced mechanical performance.
A research team from the East China University of Science and Technology has developed an AI-assisted materials-genome approach (MGA) that enables the rapid design of high-performance thermosetting polyimides. Their study (DOI: 10.1007/s10118-025-3403-x), published online on September 2, 2025, in the Chinese Journal of Polymer Science, introduces a machine-learning model capable of predicting three key mechanical parameters—Young's modulus, tensile strength, and elongation at break—across thousands of candidate structures. The approach successfully identified a new formulation, PPI-TB, whose performance surpassed well-known benchmark polyimides.
The team constructed Gaussian process regression (GPR) models trained on over 120 experimental datasets of polyimide films. Each polymer's structural fragments—dianhydride, diamine, and end-capping units—were treated as "genes," defining a vast chemical space of 1,720 phenylethynyl-terminated polyimides (PPIs). The models achieved high predictive accuracy (R² ≈ 0.70–0.74) for all three mechanical metrics and were used to score every candidate for comprehensive mechanical performance. Molecular dynamics simulations validated the screening, showing that PPI-TB (gene combination A₄/B₃₂) exhibited superior modulus (3.48 GPa), toughness, and strength indicators compared with established systems PETI-1 and O-O-3. Subsequent experiments on representative PPIs confirmed the strong consistency between predicted and measured data.
Further "gene" and feature-importance analyses revealed key design principles: conjugated aromatic structures enhance stiffness, heteroatoms and heterocycles strengthen molecular interactions, and flexible Si- or S-containing units improve elongation. Together, these insights demonstrate how integrating AI predictions with molecular interpretation can uncover structure–property rules and accelerate polymer innovation.
"By translating polymer fragments into genetic-like descriptors, we can treat molecular design like decoding a genome," said Prof. Li-Quan Wang, one of the corresponding author of the study. "Machine learning not only predicts performance but also reveals which chemical 'genes' are driving it. This synergy between data science and chemistry allows us to explore material possibilities that would take decades by conventional means. The success of PPI-TB exemplifies how AI can redefine the discovery process for next-generation high-temperature polymers.”
The AI-driven materials-genome strategy provides a universal, scalable framework for designing polymers with targeted combinations of stiffness, strength, and flexibility—traits essential to microelectronics, aerospace composites, and flexible circuit substrates. By replacing years of experimental iteration with predictive modeling and virtual screening, this method drastically reduces cost and development time. Beyond polyimides, the workflow could be adapted for other high-performance polymer classes, guiding the creation of lightweight, durable, and thermally stable materials that power future electronic and aerospace technologies.
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References
DOI
10.1007/s10118-025-3403-x
Original Source URL
https://doi.org/10.1007/s10118-025-3403-x
Funding information
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).
About Chinese Journal of Polymer Science
Chinese Journal of Polymer Science (CJPS) is a monthly journal published in English and sponsored by the Chinese Chemical Society and the Institute of Chemistry, Chinese Academy of Sciences. CJPS is edited by a distinguished Editorial Board headed by Professor Qi-Feng Zhou and supported by an International Advisory Board in which many famous active polymer scientists all over the world are included. Manuscript types include Editorials, Rapid Communications, Perspectives, Tutorials, Feature Articles, Reviews and Research Articles. According to the Journal Citation Reports, 2024 Impact Factor (IF) of CJPS is 4.0.