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.<br>
Poly(α-hydroxy acids), as a family of biodegradable polyesters, are valuable materials due to their broad applications in packaging, agriculture, and biomedical engineering. Herein we highlight and explore recent advances of catalysts in controlled ring-opening polymerization of O-carboxyanhydrides towards functionalized poly(α-hydroxy acids), especially metal catalyst-mediated controlled polymerization. Limitations of current polymerization strategies of O-carboxyanhydrides are discussed.
Isoselective Ring-opening Polymerization of Racemic Lactide Catalyzed by N-heterocyclic Olefin/(Thio)urea Organocatalysts
