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High-entropy alloys (HEAs), characterized as compositionallycomplex solid solutions with five or more metal elements, have emerged as a novelclass of catalytic materials with unique attributes. Because of the remarkablediversity of multielement sites or site ensembles stabilized by configurationalentropy, human exploration of the multidimensional design space of HEAspresents a formidable challenge, necessitating an efficient, computational and data-driven strategy over traditional trial-and-error experimentation or physics-basedmodeling. Leveraging deep learning interatomic potentials for large-scalemolecular simulations and pretrained machine learning models of surfacereactivity, our approach effectively rationalizes the enhanced activity of apreviously synthesized PdCuPtNiCo HEA nanoparticle system for electrochemicaloxygen reduction, as corroborated by experimental observations. We contend thatthis framework deepens our fundamental understanding of the surface reactivity ofhigh-entropy materials and fosters the accelerated development and synthesis of monodisperse HEA nanoparticles as a versatilematerial platform for catalyzing sustainable chemical and energy transformations. 

Metal nanocrystals (NCs) with different structural features are produced by seeded-electrodeposition.