Adsorbate-Directed Surface Phase Separation in Cu–Ag Catalysts

Résumé fourni par la source

The determination of surface configuration is essential for understanding catalytic reactivity, yet these active states cannot be reliably inferred from bulk phase diagrams. In this work, we develop a machine-learning-accelerated molecular dynamics (ML-MD) framework on the Cu-Ag system to predict and control surface structure, identifying two competing descriptors: surface energy and adsorbate binding energy. We show that Ag's lower surface energy dominates in vacuum, forming a passive sheath, whereas a CO atmosphere reverses segregation and exposes active Cu sites through lateral phase separation. Decoupling the thermodynamic driving force from the kinetic diffusion barrier, we find that stability emerges not at near-equimolar ratios where segregation is strongest but rather at dilute compositions where weak driving forces and high kinetic barriers coincide. This framework offers a generalizable strategy for balancing activity and stability in metastable catalyst design.

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