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Updated: Aug 5, 2026
09:41
Bulk and Thin Film Synthesis of Compositionally Variant Entropy-stabilized Oxides
Published on: May 29, 2018
Artificial Intelligence-Assisted Design of High-Entropy Oxide Catalysts
Ying He1, Haiyang Cheng1, Tong Zhou1
1National Center for International Joint Research of Photoelectric Energy Materials and Application, School of Energy and Materials, Yunnan University, Kunming, Yunnan, P. R. China.
Advanced Materials (Deerfield Beach, Fla.)
|July 28, 2026
Summary
High-entropy oxides (HEOs) offer vast potential for catalysis due to their complex structures. Artificial intelligence (AI) is crucial for navigating HEO complexity, enabling rational catalyst design and discovery.
Area of Science:
- Materials Science
- Catalysis
- Computational Chemistry
Background:
- High-entropy oxides (HEOs) possess unique compositional and active-site diversity compared to conventional oxides.
- Their complex nature, involving multiple cations and disordered environments, presents challenges in rational catalyst design.
- Factors like configurational entropy, mixing enthalpy, and synthesis conditions influence HEO phase formation and stability.
Purpose of the Study:
- To review the challenges and opportunities in designing high-entropy oxide catalysts.
- To examine the application of artificial intelligence (AI) in understanding and predicting HEO properties.
- To highlight the need for integrated AI approaches for multi-objective optimization in catalyst discovery.
Main Methods:
- Review of existing literature on high-entropy oxides and AI applications in catalysis.
- Discussion of data representation and model selection for AI in HEOs.
- Examination of AI's role in predicting phase stability, catalytic performance (hydrogen production, oxygen evolution, thermal, and photocatalysis), and discovering new electrocatalysts.
Main Results:
- AI effectively manages the complexity of HEOs by learning structure-property relationships.
- AI aids in predicting phase stability and catalytic performance across various applications.
- Current AI applications focus on single-property prediction, with potential for multi-objective optimization.
Conclusions:
- AI is a powerful tool for accelerating the rational design and discovery of high-entropy oxide catalysts.
- Future directions involve integrating AI with computational methods (DFT, MLIP) and experimental validation for closed-loop catalyst development.
- Moving towards multi-objective optimization using AI is essential for unlocking the full potential of HEOs.
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