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

TL;DR

This study uses machine learning to analyze over 1,000 perovskite oxide surfaces, revealing key electronic factors that influence their work functions, aiding the design of better electronic and electrochemical devices.

Contribution

It demonstrates the effectiveness of interpretable data-driven models in understanding work function determinants in cubic perovskites from limited electronic-structure data.

Findings

Work functions of BO2-terminated surfaces depend on oxygen p band energy.

Work functions of AO-terminated surfaces relate to B-site d band filling and electronic affinity.

Data-driven models provide insights into electronic factors affecting work functions.

Abstract

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.