Reactant-Induced Dynamic Active Sites on Cu Catalysts during the Water–Gas Shift Reaction

Adsorbates can trigger surface reconstruction on metal surfaces, a common yet highly important phenomenon in heterogeneous catalysis that has not been fully explored. Here, we develop a reliable Cu–C–O machine learning force field (MLFF) with ab initio accuracy, providing insights into the reconstruction mechanism and distribution of active sites on the Cu surface under a CO atmosphere through state-of-the-art deep potential molecular dynamics (DPMD). Combining statistical cluster analysis with microkinetic modeling, we establish a strategy to quantitatively assess the turnover frequency (TOF) of catalyst surfaces during the dynamic catalytic process. Our findings reveal that edge Cu atoms undergo rearrangement, ejection, diffusion, and aggregation under a CO atmosphere, leading to the formation of cluster active sites. These small clusters in dynamic equilibrium are identified as the origin of the high catalytic activity of Cu-based catalysts for a low-temperature water–gas shift reaction (WGSR). This work not only elucidates intrinsic activity in metal catalysis and the dynamic catalysis theory but also offers valuable insights for computational catalysis methods to identify effective catalysts for practical applications.