The Evolving Role of Computation and AI in Catalyst Discovery

Abstract

The discovery, development, and deployment of new materials not only provide significant business opportunities but also drive advances in high-value applications, from microelectronics to medicine. As computational chemistry evolves and AI systems gain prominence, their influence on materials discovery—particularly in catalyst design and polymer-forming reactions—is becoming transformative. We have developed a broad class of highly active organic catalysts that operate across a wide range of monomers suitable for ring-opening polymerization as well as polymer recycling. By combining fundamental mechanistic studies with AI-assisted modeling, we have uncovered new pathways to creating well-defined macromolecular architectures.<br/>To address the challenges of time-to-market, the integration of automated synthesis, high-throughput characterization, and AI-driven predictive models into a unified pipeline holds the potential to radically accelerate the discovery and optimization of catalysts. This approach allows for the rapid exploration of vast chemical spaces and the identification of optimal catalysts at a fraction of the time and cost required by traditional methods, positioning AI as a central tool in the next generation of materials development.