Balancing stiffness, strength, and toughness in thermosetting polyimide films has long challenged materials scientists. In a study published online on September 2, 2025, in the Chinese Journal of Polymer Science (DOI: 10.1007/s10118-025-3403-x), researchers from East China University of Science and Technology developed an AI-assisted materials-genome approach that enables rapid design of high-performance thermosetting polyimides.
The team constructed Gaussian process regression 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 Young's modulus, tensile strength, and elongation at break, 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 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.
"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 authors. "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.
This research 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). The study was published in the Chinese Journal of Polymer Science, a monthly journal sponsored by the Chinese Chemical Society and the Institute of Chemistry, Chinese Academy of Sciences, with a 2024 Impact Factor of 4.0.
