AI-Driven Materials Genome Approach Accelerates Development of High-Performance Polymer Films

Balancing stiffness, strength, and toughness in thermosetting polyimide films has long challenged materials scientists. A research team from East China University of Science and Technology has developed an AI-assisted materials-genome approach that enables the rapid design of high-performance thermosetting polyimides. Their study published in the Chinese Journal of Polymer Science introduces a machine-learning model capable of predicting three key mechanical parameters across thousands of candidate structures.

The approach successfully identified a new formulation, PPI-TB, whose performance surpassed well-known benchmark polyimides. 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 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: conjugated aromatic structures enhance stiffness, heteroatoms and heterocycles strengthen molecular interactions, and flexible units improve elongation. Together, these insights demonstrate how integrating AI predictions with molecular interpretation can uncover structure-property rules and accelerate polymer innovation.

The study details are available in the original publication at https://doi.org/10.1007/s10118-025-3403-x. This 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.