  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 on catalyst performance because the
surface of metal oxide catalysts often acts as reaction sites, 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 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 on whether the surface
is (111), and the ionization potential, followed by geometrical descriptors
that are different in each O site.
