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We introduce a framework integrating ML energy prediction, thermodynamic sampling, Bayesian optimization, and kinetic Monte Carlo (kMC) to navigate the vast compositional space of high-entropy alloy nanoparticles for hydrogen evolution. Static adsorption energy-based screening favored simpler, Pt-rich alloys, while dynamic kMC evaluation identified a complex Au$_{33}$Co$_{20}$Cu$_{6}$Pd$_{26}$ nanoparticle as optimal, revealing a multisite reaction mechanism involving adsorption, migration, and desorption at distinct elemental environments. This highlights the critical role of kinetic simulations in uncovering superior catalytic compositions beyond static thermodynamic predictions.
Dynamic kinetic evaluation uncovers a complex multicomponent alloy nanoparticle as superior for hydrogen evolution, challenging static adsorption energy-based design principles.
High-entropy alloy (HEA) nanoparticles offer chemically diverse catalytic sites, but identifying optimal compositions across their large design space remains challenging. We developed a framework combining machine-learning energy prediction, thermodynamic Monte Carlo sampling, Bayesian optimization, and kinetic Monte Carlo (kMC) simulations to screen 85-atom nanoparticle models across a nine-element composition space for the hydrogen evolution reaction. Static optimization based on adsorption energies selected Pt-rich, low-component compositions, whereas kMC-based optimization ranked the multielement composition Au$_{33}$Co$_{20}$Cu$_{6}$Pd$_{26}$ highest among the evaluated candidates. Cycle-resolved analysis further showed hydrogen adsorption at Co/Pd-rich environments, migration through intermediate-binding sites, and frequent Heyrovsky desorption at weaker-binding, Au-containing environments. This pathway-level evidence supports a multisite reaction picture and illustrates how incorporating kinetics can complement static adsorption-energy screening.