  Tuning the work functions of materials is of practical interest for
maximizing the performance of microelectronic and (photo)electrochemical
devices, as the efficiency of these systems depends on the ability to control
electronic levels at surfaces and across interfaces. Perovskites are promising
compounds to achieve such control. In this work, we examine the work functions
of more than 1,000 perovskite oxide surfaces (ABO$_3$) by data-driven
(machine-learning) analysis and identify the factors that determine their
magnitude. While the work functions of BO$_2$-terminated surfaces are sensitive
to the energy of the hybridized oxygen p bands, the work functions of
AO-terminated surfaces exhibit a much less trivial dependence with respect to
the 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 analyzing
the work functions of cubic perovskites from a limited number of
electronic-structure descriptors.
