A Recommender System for Inverse Design of Polycarbonates and Polyesters

Abstract

The convergence of artificial intelligence and machine learning with material science holds significant promise in rapidly accelerating the development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties or characteristics. Following the recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity for polyesters or polycarbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.