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Accessing cyclic carbonate monomers on a large scale is critical for the development of any new carbonate-based materials platform. The synthesis of carbonate monomers can be a challenging and tedious endeavor requiring multiple synthetic steps and purifications. To address this, we report a drastically improved process for the synthesis of carbonate monomers via a two-step route that avoids the use of hazardous triphosgene or chloroformate reagents. This process enables rapid access to a broad array of functional groups on the carbonate monomer and the monomers generated from the procedure can readily be polymerized via ring-opening polymerization.

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.