Work

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

Year: 2026

Type: article

Abstract: 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.

Source: Physical Review Materials

DOI: 10.1103/wjcq-jv1c

Authors: Chiho Kim, Wei Xiong, Akhlak Mahmood, Rampi Ramprasad, Huan Tran

Institution: Georgia Institute of Technology

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Responsible consumption and production

Open Access status: green

Funders: Multidisciplinary University Research Initiative, Office of Naval Research