Unravelling the Impact of Metal Dopants and Oxygen Vacancies on Syngas Conversion over Oxides: A Machine Learning-Accelerated Study of CO Activation on Cr-Doped ZnO Surfaces

As a critical component
of the OX–ZEO composite catalysts
toward syngas conversion, the Cr-doped ZnO ternary system can be considered
as a model system for understanding oxide catalysts. However, due
to the complexity of its structures, traditional approaches, both
experimental and theoretical, encounter significant challenges. Herein,
we employ machine learning-accelerated methods, including grand canonical
Monte Carlo and genetic algorithm, to explore the ZnO(1010) surface with various Cr and oxygen vacancy (OV) concentrations.
Stable surfaces with varied Cr and OV concentrations were then systematically
investigated to examine their influence on the CO activation via density
functional theory calculations. We observe that Cr tends to preferentially
appear on the surface of ZnO(1010) rather than
in its interior regions and Cr-doped structures incline to form rectangular
islands along the [0001] direction at high Cr and OV concentrations.
Additionally, detailed calculations of CO reactivity unveil an inverse
relationship between the reaction barrier (Ea) for C–O bond dissociation and the Cr and OV concentrations,
and a linear relationship is observed between OV formation energy
and Ea for CO activation. Further analyses
indicate that the C–O bond dissociation is much more favored
when the adjacent OVs are geometrically aligned in the [1210] direction, and Cr is doped around the reactive sites.
These findings provide a deeper insight into CO activation over the
Cr-doped ZnO surface and offer valuable guidance for the rational
design of effective catalysts for syngas conversion.