Abstract

Understanding how the composition and structure of heterogeneous catalysts change during in situ reaction is of critical importance but greatly challenging because of the complexity of multiple time–space scale. Herein, we propose a self-adaptive simulation strategy driven by the first-principles microkinetic modeling and genetic-algorithm-based global structural search accelerated by machine learning that allows the interplay of surface reaction and catalyst evolution, and uncover the structural/compositional evolution of a Pd(111) single-crystal catalyst under the reaction conditions for CO oxidation, which is a classic open problem lasting for decades. The possible active phases at the kinetically steady state are identified and their common nature is unraveled. We show that, in the presence of CO/O2 reaction mixtures, a dynamically stable partially oxidized nonstoichiometric palladium oxide (PdO0.44 layer) grown on Pd(111) is formed with the O adatoms inserted into the Pd(111) sublayer that drives the formation of the surface oxide. Remarkably, the first-principles microkinetic analyses demonstrate that this self-evolved PdO0.44 surface at the steady state exhibits higher catalytic activity for CO oxidation as compared with Pd(111) and overoxidized PdO catalyst, which may explain the long-standing puzzle of the “PdOx” active phase for Pd catalyst during CO oxidation.

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