AI-Driven Materials Genome Strategy Accelerates Design of Ultra-Tough Polyimide Films

Balancing stiffness, strength, and toughness in thermosetting polyimide films has long challenged materials scientists. In a new study, researchers combined machine learning with a materials-genome framework to rapidly predict and optimize these competing properties. By defining polymer substructures as molecular genes, they screened more than 1,700 phenylethynyl-terminated polyimide candidates and identified one formulation, PPI-TB, with simultaneously high Young's modulus, tensile strength, and elongation at break.

Polyimide films are essential in aerospace, flexible electronics, and micro-display technologies for their thermal stability and insulation. However, mechanical optimization remains elusive as 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 research team 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 study published in Chinese Journal of Polymer Science introduces a machine-learning model capable of predicting three key mechanical parameters across thousands of candidate structures. The team constructed Gaussian process regression models trained on over 120 experimental datasets of polyimide films. Each polymer's structural fragments were treated as genes, defining a vast chemical space of 1,720 phenylethynyl-terminated polyimides. The models achieved high predictive accuracy 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 exhibited superior modulus, toughness, and strength indicators compared with established systems. Subsequent experiments on representative polyimides confirmed the strong consistency between predicted and measured data. Further gene and feature-importance analyses revealed key design principles where conjugated aromatic structures enhance stiffness, heteroatoms and heterocycles strengthen molecular interactions, and flexible units improve elongation.

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. The complete research findings are available at https://doi.org/10.1007/s10118-025-3403-x.

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 integrated approach demonstrates how combining artificial intelligence with molecular interpretation can uncover structure-property rules and accelerate polymer innovation across multiple industrial sectors.