AI-driven design of poly(ethylene terephthalate) replacement copolymers

A widely used thermoplastic, poly(ethylene terephthalate) (PET), faces increasing environmental and regulatory pressure, motivating the search for viable alternatives. Here, the authors present an AI-driven polymer design pipeline implemented using the PolymRize. The framework combines virtual forward synthesis with machine learning to generate PET-replacement copolymers. Inspired by the esterification route of PET synthesis, more than 12,000 candidate polymers were systematically constructed from TSCA-listed monomers. ML models predicted glass transition temperature, bandgap, and crystallization tendency to enable multi-objective screening. The approach rediscovered known PET alternatives and identified previously unknown candidates, several of which were synthesized and experimentally validated.