From Prediction to Performance: A Roadmap for Single-Atom Alloy Catalysts
Dennis Meier, E. Charles H. Sykes
Abstract
Single-atom alloy (SAA) catalysts bridge the precision of homogeneous catalysis and the practicality of heterogeneous systems. This Perspective highlights how theory-led approaches, particularly density functional theory and machine learning, enable their rational design. By shifting from combinatorial exploration to targeted prediction, researchers have identified alloy combinations with high activity, selectivity, and stability that would have not been predicted based on conventional catalytic wisdom. The unique geometric and electronic structures of SAAs break scaling relationships and enable bifunctional reactivity. Emerging areas such as dual-atom alloys and plasmon-enhanced and electrochemical catalysis are discussed. As the compositional phase space of bimetallic and trimetallic systems grows exponentially, theoretical guidance is essential to navigate this complexity efficiently. With surface science providing crucial mechanistic insights and AI accelerating screening, we outline a roadmap for predictive catalyst design and advocate for tighter integration between computation and experiment to address pressing challenges in energy and environmental catalysis.