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Multinary metal alloy catalysts can provide unprecedented tunability in catalyst design, but their optimization is challenging due to the vastness of the combinatorial design space. In a recent issue of Angewandte Chemie International Edition, Rossmeisl and coworkers used a computational framework combining ab initio calculations, kinetic modeling, and Bayesian optimization to efficiently optimize fuel cell catalysts by first quantifying the number of trials needed and then executing an efficient search.