A Recommender System for Inverse Design of Polycarbonates and Polyesters

22 July 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate 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. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.

Keywords

Inverse Design
Ring-Opening Polymerization
Polycarbonates
Polyesters

Supplementary materials

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Supporting Information–Recommender
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