Artificial Intelligence-Assisted Design of High-Entropy Oxide Catalysts

High-entropy oxides (HEOs), which contain multiple cations with diverse valence states and highly disordered local environments, offer a much broader compositional and active-site space than conventional single-component oxides. This diversity creates opportunities for tailoring catalytic properties, but it also makes rational design considerably more difficult. The formation and stability of HEOs phases depend on the combined effects of configurational entropy, mixing enthalpy, oxidation potential, valence compatibility, and synthesis conditions. Meanwhile, their catalytic behavior is often controlled by oxygen vacancies, surface segregation, in situ reconstruction, and dynamically evolving active sites under operating conditions. Artificial intelligence (AI) provides a promising means of managing this complexity by learning relationships among composition, structure, defects, adsorption behavior, and catalytic performance. This review summarizes the major challenges in HEOs catalyst design, discusses data representation and model selection, and examines AI applications in phase stability prediction, hydrogen production, oxygen evolution, thermal catalysis, photocatalysis, and electrocatalyst discovery. It further emphasizes the need to move beyond single-property prediction toward multi-objective optimization and closed-loop integration of AI, DFT, MLIP-based sampling, operando characterization, and experimental validation.