Machine Learning-Guided Design of High Entropy Oxides for Photo-Electrocatalysis and Sustainable Energy Conversion
8 affiliations
- 1School of Environmental Science and Engineering, Yangzhou University, Yangzhou 225127, China
- 2School of Physical Science and Technology, Yangzhou University, Yangzhou 225009, China
- 3Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, Industrial Chemistry Section, University of Messina, ERIC aisbl, INSTM/CASPE, Viale F. Stagno d’Alcontres 31, 98166 Messina, Italy
- 4Department of Physics, Qilu Institute of Technology, Jinan 250200, China
- 5Wolfson Institute for Biomedical Research, Division of Medicine, University College London, Gower St., London WC1E 6BT, UK
- 6Department of Computer Engineering, Biruni University, Istanbul 34010, Turkey
- 7Mathematics Research Center, Near East University, Nicosia 99138, Cyprus
- 8Department of Management Information Systems, College of Business and Economics, Qassim University, Buraydah 51452, Saudi Arabia
Abstract
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.
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BibTeX
@article{bilal2026machine,
title = {{Machine Learning-Guided Design of High Entropy Oxides for Photo-Electrocatalysis and Sustainable Energy Conversion}},
author = {Muhammad Bilal and Muhammad Sabir and Niaz Wali and Hao Sun and Yakup Yildirim and Taha Radwan},
journal = {Catalysts},
year = {2026},
month = oct,
doi = {10.3390/catal16110972},
url = {https://www.mdpi.com/2073-4344/16/11/972},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Machine Learning-Guided Design of High Entropy Oxides for Photo-Electrocatalysis and Sustainable Energy Conversion
AU - Muhammad Bilal
AU - Muhammad Sabir
AU - Niaz Wali
AU - Hao Sun
AU - Yakup Yildirim
AU - Taha Radwan
PY - 2026
DA - 2026/10/31
JO - Catalysts
DO - 10.3390/catal16110972
UR - https://www.mdpi.com/2073-4344/16/11/972
AB - 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.
SN - 2073-4344
ER -