Comprehensive Study of Oxygen Vacancies on the Catalytic Performance of ZnO for CO/H2Activation Using Machine Learning-Accelerated First-Principles Simulations

Abstract

Oxygen vacancies (OVs) play important roles on any oxide catalysts. In this work, using an investigation of the OV effects on ZnO(101¯ 0) for CO and H2activation as an example, we demonstrate, via machine learning potentials (MLPs), genetic algorithm (GA)-based global optimization, and density functional theory (DFT) validations, that the ZnO(101¯ 0) surface with 0.33 ML OVs is the most likely surface configuration under experimental conditions (673 K and 2.5 MPa syngas (H2:CO = 1.5)). It is found that a surface reconstruction from the wurtzite structure to a body-centered-tetragonal one would occur in the presence of OVs. We show that the OVs create a Zn3cluster site, allowing H2homolysis and C-O bond cleavage to occur. Furthermore, the activity of intrinsic sites (Zn3cand O3csites) is almost invariable, while the activity of the generated OV sites is strongly dependent on the concentration of the OVs. It is also found that OV distributions on the surface can considerably affect the reactions; the barrier of C-O bond dissociation is significantly reduced when the OVs are aligned along the [12¯ 10] direction. These findings may be general in the systems with metal oxides in heterogeneous catalysis and may have significant impacts on the field of catalyst design by regulating the concentration and distribution of the OVs.