UCLA

Heterogeneous catalysts play a key role in the chemical and energy industries 1 . To date, most industrial-scale heterogeneous catalytic reactions have relied on nanocatalysts 2,3 . However, despite significant progress from theoretical, experimental and computational studies 4-18 , identifying the active sites of alloy nanocatalysts remains a major challenge. This limitation is mainly due to an incomplete understanding of the three-dimensional (3D) atomic and chemical arrangement of different constituents and structural reconstructions driven by catalytic reactions 19-22 . Here, we use atomic electron tomography 23 to determine the 3D local atomic structure, surface morphology and chemical composition of 11 Pt alloy nanocatalysts for the electrochemical oxygen reduction reaction (ORR). We reveal the facet, surface concaveness, structural and chemical order/disorder, coordination number, and bond length with unprecedented 3D atomic detail. The experimental 3D atomic coordinates are used by first-principles trained machine learning to identify the active sites of the nanocatalysts, which are corroborated by electrochemical measurements. A striking feature is the difference of the ORR activity of the surface Pt sites on the nanocatalysts by several orders of magnitude. Furthermore, by analyzing the structure-activity relationship, we formulate an equation named the local environment descriptor to balance the strain and ligand effects and gain quantitative insights into the ORR active sites of the Pt alloy nanocatalysts. The ability to determine the 3D atomic structure and chemical composition of realistic nanoparticles coupled with machine learning could transform our fundamental understanding of the catalytic active sites and provide a guidance for the rational design of optimal nanocatalysts.