Optimization of the facet structure of transition-metal catalysts applied to the oxygen reduction reaction

Predicting the optimal structure for a catalytic material has been a long-standing goal, but typically an arbitrary active site on a uniform surface is modelled. Identification of the most-active facet structure for structure-sensitive chemistries, such as the oxygen reduction reaction, is lacking. Here we develop an approach to predict the optimal structure of a catalytic material by identifying the active site and identifying the density and spatial arrangement of such sites while minimizing the surface energy. We find that the theoretical peak performance predicted by linear scaling relations is unattainable because of the lack of suitable active sites on low-index planes, as well as geometric and stability constraints. A random array of vacancies results in a modest performance enhancement compared to ideal facets, whereas defect sites with a maximum density in disordered structures significantly increase the catalyst performance. We applied this methodology to the oxygen reduction reaction on defected Pt(111), Pt(100), Au(111) and Au(100) surfaces.