Invited talk

Evolution of catalysis at IBM: From microelectronics to biomedicine to sustainability with AI-driven innovation

Abstract

The discovery and development of new materials continue to drive innovation in fields ranging from microelectronics to medicine. While many polymer-forming reactions have been extensively exploited, advances in computational chemistry have reinvigorated polymer science, allowing for new insights into polymer-forming reactions, catalysis, and supramolecular assemblies. Catalysis, a cornerstone of sustainable chemical processes, remains a key area of focus, with the development of environmentally benign catalytic systems becoming central to Green Chemistry.

Our research has centered on creating a class of highly efficient organic catalysts for synthesizing biodegradable and biocompatible plastics. These catalysts, developed through the convergence of experimental and computational chemistry, enable the production of polymers with precise control over molecular weight, end-group fidelity, and backbone functionality. Mechanistic studies have revealed diverse pathways for organocatalytic polymerizations, opening new avenues for designing well-defined macromolecular architectures.

The focus on renewable monomer feedstocks, such as lactides, lactones, and carbonates, along with traditional petrochemical sources, has driven the application of these polymers in areas like nanomedicine and macromolecular therapeutics. These materials, with their tailored degradation profiles and functionalization, are particularly suited for drug delivery systems, enabling precise therapeutic targeting.

Recently, the integration of AI and machine learning into catalyst design has accelerated the discovery of novel catalytic systems. By combining computational modeling with experimental validation, we have enhanced our ability to optimize polymerization conditions, driving further advancements in sustainable polymer chemistry and medical applications. This evolution represents the future of materials science, where AI-assisted approaches are set to revolutionize the development of catalysts and macromolecular systems.

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