Abstract

Spinel oxides are an important class of materials for heterogeneous catalysis including photocatalysis and electrocatalysis. The surface O vacancy formation energy (EOvac) is a critical quantity for catalyst performance because the surface of metal oxide catalysts often acts as a reaction site, for example, in the Mars-van Krevelen mechanism. However, experimental evaluation of EOvac is very challenging. We obtained the EOvac for (100), (110), and (111) surfaces of normal zinc-based spinel oxides ZnAl2O4, ZnGa2O4, ZnIn2O4, ZnV2O4, ZnCr2O4, ZnMn2O4, ZnFe2O4, and ZnCo2O4. The most stable surface is (100) for all compounds. The smallest EOvac for a surface is the largest in the (100) surface except for ZnCo2O4. For (100) and (110) surfaces, there is a good correlation, over all spinels, between the smallest EOvac for the surface and bulk formation energy, while the ionization potential correlates well in (111) surfaces. Machine learning over EOvac of all surface sites in all orientations and for all compounds to find the important factors, or descriptors, that decide the EOvac revealed that bulk and surface-dependent descriptors are the most important, namely the bulk formation energy, a Boolean descriptor of whether the surface is (111) or not, and the ionization potential, followed by geometrical descriptors that are different in each O site.

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