PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers.

ABSTRACT: A necessary transformation for a sustainable economy is the transition from fossil-derived plastics to polymers derived from biomass and waste resources. While renewable feedstocks can enhance material performance through unique chemical moieties, probing the vast material design space by experiment alone is not practically feasible. Here, we develop a machine-learning-based tool, PolyID, to reduce the design space of renewable feedstocks to enable efficient discovery of performance-advantaged, biobased polymers. PolyID is a multioutput, graph neural network specifically designed to increase accuracy and to enable quantitative structure-property relationship (QSPR) analysis for polymers. It includes a novel domain-of-validity method that was developed and applied to demonstrate how gaps in

SUBMITTER: Wilson AN 

PROVIDER: S-EPMC10653284 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature