PME Materials Special Seminar- Jin Huang

Catalysts drive the chemical transformations that define our modern lives, yet realizing the clean energy, industrial chemicals, and consumer products of the future will require breakthrough materials discovered through innovative methods. Conventional discovery relies on trial-and-error, slowing down the process. In this talk, I will introduce two powerful approaches to accelerate catalyst discovery. First, I will present an experimentally attainable descriptor that predicts the catalytic activity and stability of Pt-alloy catalysts for the oxygen reduction reaction (ORR) in fuel cells, enabling the rapid identification of efficient catalysts. Second, I will discuss the development of a “megalibrary” materials platform comprising of over a hundred million nanoparticles. By combining high-throughput combinatorial screening, megalibraries, and AI, we identify high-performance non-Ir catalysts for the oxygen evolution reaction (OER) in water electrolyzers. Together, these approaches demonstrate how integrating theory, high-throughput materials platforms, and AI accelerates materials discovery for energy technologies and beyond.