FastCat: Autonomous Discovery of Multielement Layered Double Hydroxide Alloy Catalysts for Alkaline Oxygen Evolution Reaction

The search for efficient, durable, and scalable catalysts for the oxygen evolution reaction (OER) is hampered by the slow innovation cycles for advanced materials. Here, we present FastCat —an artificial intelligence (AI)‐orchestrated self‐driving closed‐loop materials discovery system for the autonomous discovery of platinum group metal free multimetal hydroxides for alkaline water electrolysis. With FastCat, we have synthesized, characterized, and tested more than 500 Ni‐based multielement layered double hydroxide OER catalysts. In one of the largest AI‐orchestrated catalyst discovery campaigns to date, our metaheuristic Bayesian optimization identified known high‐performance compositions and several novel multielement Ni–Fe–Cr–Co alloys with unprecedented overpotentials as low as 231 mV at 20 mA cm −2 and compositional ratios approaching at higher current densities. FastCat can synthesize and electrochemically test up to 75 material compositions daily without human interaction. It can optimize the composition by analyzing trends in the test results, validating their reproducibility, and testing durability. This study includes measurements of >1000 samples, making it one of the largest autonomous experimental electrocatalysis datasets to date, and the findings showcase how autonomous materials discovery can make complex, multielement compositions accessible for research and drastically accelerate the development of novel materials.