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Tuning the work functions of materials is of practical interest formaximizing the performance of microelectronic and (photo)electrochemicaldevices, as the efficiency of these systems depends on the ability to controlelectronic levels at surfaces and across interfaces. Perovskites are promisingcompounds to achieve such control. In this work, we examine the work functionsof more than 1,000 perovskite oxide surfaces (ABO$_3$) by data-driven(machine-learning) analysis and identify the factors that determine theirmagnitude. While the work functions of BO$_2$-terminated surfaces are sensitiveto the energy of the hybridized oxygen p bands, the work functions ofAO-terminated surfaces exhibit a much less trivial dependence with respect tothe filling of the d bands of the B-site atom and of its electronic affinity.This study shows the utility of interpretable data-driven models in analyzingthe work functions of cubic perovskites from a limited number ofelectronic-structure descriptors.

Data-driven analysis of the electronic-structure factors controlling the work functions of perovskite oxides