Accelerating the development of new polymeric materials: The convergance of automated experimentation, cloud computing, and predictive modeling

Abstract

Accelerating the development of high-performance materials requires advancements not only in the fundamental synthetic chemistry and catalysis, but also the surrounding laboratory infrastructure. Here, we demonstrate how our recent work on the integration of new methods for scalable carbonate monomer synthesis, rapid and automated continuous-flow ring-opening and step-growth polymerization, predictive modeling of historical polymerization data, the creation material-focused database infrastructure, and the use of the IBM hybrid cloud computing platforms enables precision synthesis of new materials with tailored properties and characteristics. This work emphasizes how convergence of automated experimentation, cloud computing, and predictive modeling is imperative in enhancing the rate of discovery within polymer chemistry and reducing experimental overhead required to access new, high-performance materials.