Data-driven analysis of the electronic-structure factors controlling the work functions of perovskite oxides. Issue 11 (16th March 2021)

• Record Type: Journal Article
• Authors: Xiong, Yihuang; Chen, Weinan; Guo, Wenbo; Wei, Hua; Dabo, Ismaila
• Is Part Of: Physical chemistry chemical physics. Volume 23:Issue 11(2021)
• Page Start: 6880
• Page End: 6887
• Publication Date: 2021-03-16
• Subjects: Chemistry, Physical and theoretical -- Periodicals; 541.3
• DOI: 10.1039/d0cp05595f
• Languages: English
• ISSNs: 1463-9076

Abstract

Abstract : Tuning the work functions of materials is critical to the performance of microelectronic and (photo)electrochemical devices. We validate data-driven models to predict the work functions of cubic perovskites from simple electronic descriptors. 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 1000 perovskite oxide surfaces (ABO3 ) using data-driven (machine-learning) analysis and identify the factors that determine their magnitude. While the work functions of the BO2 -terminated surfaces are sensitive to the energy of the hybridized oxygen p bands, the work functions of the 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.