Machine Learning-Guided Design of High Entropy Oxides for Photo-Electrocatalysis and Sustainable Energy Conversion

High-entropy oxides (HEOs) offer a vast compositional platform for photo-electrocatalysis and sustainable energy conversion, where multicationic disorder can tune phase stability, electronic structure, defect chemistry, light absorption, charge transport, and surface reactivity. However, the enormous number of possible compositions, local configurations, and synthesis conditions limits conventional trial-and-error discovery. This review critically evaluates how machine learning (ML) accelerates HEO design, from phase and structure classification, oxygen-vacancy prediction, machine learning interatomic potentials, and active learning to photocatalytic, electrocatalytic, thermocatalytic, and energy-storage applications. Unlike earlier surveys, it grades the evidence by separating studies that report accurate ML predictions from those in which ML-selected HEOs were synthesized and shown to perform; benchmarks representative models together with their dataset sizes and validation procedures; and draws on high-entropy alloy catalysis to show how local atomic environments shape adsorption-energy distributions, reaction barriers, and selectivity. This appraisal shows that functional validation of ML-selected HEO catalysts has so far been achieved mainly for CH4 oxidation, the water–gas shift reaction, and the oxygen evolution reaction, whereas photocatalytic H2 evolution and CO2 conversion still lack prospective ML validation. Key challenges include biased datasets, incomplete synthesis metadata, inadequate representations of local disorder, and uncertain applicability domains. Finally, complementary validation categories and a minimum reporting standard are proposed to support reliable ML-guided discovery of synthesizable HEOs for sustainable energy technologies.