AI-Driven Materials Genome Strategy Unlocks Breakthrough in High-Performance Polymer Films

Materials scientists have long struggled to balance competing mechanical properties in thermosetting polyimide films, where improving stiffness often reduces toughness and enhancing one characteristic typically compromises others. Traditional trial-and-error synthesis approaches have proven slow, costly, and limited in exploring complex molecular spaces. Now, researchers from East China University of Science and Technology have developed an AI-assisted materials-genome approach that enables rapid design of high-performance thermosetting polyimides with superior, well-balanced mechanical performance.

The research team's study published 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. By treating polymer structural fragments as molecular "genes," the researchers screened more than 1,720 phenylethynyl-terminated polyimide candidates and identified one formulation, PPI-TB, that simultaneously achieves high Young's modulus, tensile strength, and elongation at break. The complete study details are available at https://doi.org/10.1007/s10118-025-3403-x.

The team constructed Gaussian process regression models trained on over 120 experimental datasets of polyimide films, achieving high predictive accuracy for all three mechanical metrics. Molecular dynamics simulations validated the screening process, showing that PPI-TB exhibited superior modulus, toughness, and strength indicators compared with established benchmark systems. Subsequent laboratory experiments on representative polyimides confirmed strong consistency between predicted and measured data, demonstrating the reliability of the AI-driven approach.

Further analysis revealed key design principles that drive optimal performance: conjugated aromatic structures enhance stiffness, heteroatoms and heterocycles strengthen molecular interactions, and flexible silicon- or sulfur-containing units improve elongation. These insights demonstrate how integrating AI predictions with molecular interpretation can uncover structure-property relationships and accelerate polymer innovation. The research was financially supported by the National Key R&D Program of China and the National Natural Science Foundation of China.

This breakthrough matters because polyimide films are essential components in aerospace, flexible electronics, and micro-display technologies, valued for their thermal stability and insulation properties. The ability to rapidly design materials with targeted combinations of stiffness, strength, and flexibility could transform manufacturing processes across multiple industries. By replacing years of experimental iteration with predictive modeling and virtual screening, this method drastically reduces development costs and timeframes while enabling the creation of materials previously considered unattainable through conventional approaches.

The implications extend beyond immediate applications in microelectronics, aerospace composites, and flexible circuit substrates. The AI-driven materials-genome strategy provides a universal, scalable framework that 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 represents a significant shift from traditional materials discovery toward data-driven design, potentially accelerating innovation cycles across the materials science landscape and enabling faster development of advanced materials for next-generation technologies.