Inverse catalysts: tuning the composition and structure of oxide clusters through the metal support

Abstract

Computational modeling of metal–oxide interfaces is challenging due to the large search space of compositions and structures and the complexity of catalyst materials under operating conditions in general. In this work, we develop an efficient structure search workflow to discover chemically unique and relevant nanocluster geometries of inverse catalysts and apply it to Zn_yO_x and In_yO_x on Cu(111), Pd(111), and Au(111). We show that the workflow is successful in obtaining a large range of chemically distinct structures. Structural geometry trends are identified, including stable motifs such as tripod, rhombus, and pyramidal motifs. Using ab initio thermodynamics, we explore the in situ stability of the structures, including single-atom alloys, at a range of oxygen availabilities. This approach allows us to find trends such as the susceptibility to oxidation of the different systems and the range of stability of different cluster motifs. Our analysis highlights the importance of taking the diversity of sites exposed by metal–oxide interfaces into account in catalyst design studies.