Self-Organization of Alloy Catalysts into Selectively Active Ensembles

The active sites of alloy catalysts may emerge only under reaction conditions, yet how reactive atmospheres select these sites remains unresolved. Here, we reveal how reactive atmospheres reorganize complex alloy surfaces into functional active-state distributions by using a machine-learning-potential-accelerated multiscale framework. Using selective acetylene hydrogenation on Pd-Ag alloys as a model reaction, we show that adsorbates reverse the intrinsic Ag-segregation tendency, enrich Pd in the outermost layer, and generate a distribution of Pd₃ hollow ensembles with distinct second-shell coordination environments. These dynamically formed ensembles, rather than the as-prepared isolated Pd sites, govern the calculated activity and selectivity trends. Ensemble-resolved energetics and kinetics identify the adsorption free-energy gap between ethylene and acetylene as a predictive descriptor that captures the activity-selectivity trade-off and defines an optimal window balancing acetylene hydrogenation and ethylene desorption. Extending the analysis across multiple alloy families further reveals a general scaling relationship in which adsorbate-driven self-organization is governed by adsorption asymmetry and alloy stability. These results establish dynamic ensemble selection as a transferable framework for understanding and designing adaptive alloy catalysts, shifting catalyst optimization from static structural descriptors toward reaction-condition-directed control of active-state distributions.