Application of Organic Catalysis and Sustainable Chemistry Towards Materials Discovery

Abstract

The discovery, development and deployment of new materials provides new business opportunities as well as to drive advances in high value applications ranging from microelectronics to medicine. Polymer science continues to be perceived as a mature field since most of the efficient polymer-forming reactions have been exploited. As advances in computational chemistry and AI systems continue, there influence on materials development on multiple length scales, the creation and understanding of new polymer-forming reactions, catalysts discovery and the modeling of supramolecular assemblies are becoming more pervasive. For example, catalysis is a foundational pillar for sustainable chemical processes; the discovery of highly active, environmentally benign catalytic processes is a central goal of Green Chemistry. Together with Robert Waymouth (Stanford University) we have developed a broad class of highly active, environmentally benign organic catalysts for the synthesis of biodegradable and biocompatible plastics that was largely driven by the convergence of experimental and computational chemistries. Fundamental mechanistic and theoretical investigations have provided new scientific insights on the diversity of mechanistic pathways for organocatalytic polymerization reactions and the opportunities that these new insights have created for the synthesis of well-defined macromolecular architectures. The monomer feedstocks have focused on those from renewable resources such as lactides, lactones and carbonates, but also from petrochemical feedstocks. The recent advances in catalyst development that span many orders of magnitude over a large palette of monomers offers a unique opportunity for rapid materials discovery, as the future of materials research will be conducted in an AI-driven, automated laboratory. Historically at IBM, materials workflows are very targeted on internal applications such as lithography and interlayer dielectrics that move rapidly towards devices. However, the commercialization of new materials in the general application space is traditionally very slow. From the discovery phase to market placement, materials development timelines are labor-intensive and require massive capital expenditure. To overcome this challenge, the merging of automated synthesis, high-throughput characterization, and predictive AI into a single pipeline offers the opportunity to dramatically accelerate materials development at a fraction of the traditional cost.