Assessing catalytic rates of bimetallic nanoparticles with active-site specificity: A case study using NO decomposition

Abstract

Bimetallic alloys have emerged as an important class of catalytic materials and span a wide range of shapes, sizes, and compositions. The combinatorics across this wide materials space makes predicting catalytic turnovers of individual active sites challenging. Herein, we introduce the stability of active sites as a descriptor for site-resolved reaction rates. The site stability unifies structural and compositional variations in a single descriptor. We compute this descriptor by using coordination-based models trained with density functional theory (DFT) calculations. Our approach enables instantaneous predictions of catalytic turnovers for nanostructures up to 12 nm in size. Using NO decomposition as the probe reaction, we identify sites on AuPt nanoparticles that, because of local structure and composition, yield rates that are one to two orders of magnitude higher than those from sites on monometallic Pt. By prescribing specific sizes, morphologies, and compositions of optimal catalytic nanoparticles, our method guides experiments toward designing bimetallic catalysts with optimal turnovers.