Triple‐Atom and High‐Entropy Catalysts: Advanced Architectures for Efficient Thermally Driven Organic Transformations

Abstract Heterogeneous catalysis is essential for advancing sustainable chemical processes, with recent research highlighting the importance of atomic‐level precision and compositional complexity in optimizing catalytic performance. This review explores two emerging classes of catalysts—triple‐atom catalysts (TACs) and high‐entropy catalysts (HECs)—which have shown considerable promise in organic transformations. Notably, this review establishes a conceptual framework by positioning TACs as a strategic intermediate that bridges the well‐defined diatomic synergy of dual‐atom catalysts (DACs) and the configurational complexity of HECs. This unique positioning enables TACs to combine atomic precision with enhanced three‐center cooperativity, while HECs employ entropy‐driven stabilization to achieve robust catalytic activity. In conjunction with density functional theory calculations, mechanistic insights into the structure–performance relationships of these catalyst classes in representative transformations are comprehensively analyzed, with particular emphasis on their complementary roles in multistep reactions. The review first outlines the multifunctional active sites and innovative synthesis strategies of both catalyst families, emphasizing how TACs bridge the gap between DACs and HECs. Subsequently, advanced characterization techniques and the integration of machine learning approaches for rational catalyst design are discussed. Finally, based on the analysis and insights presented, future directions for development in this field are proposed.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-12-12
DOI
https://doi.org/10.1002/adfm.202524149
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
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article

Triple‐Atom and High‐Entropy Catalysts: Advanced Architectures for Efficient Thermally Driven Organic Transformations

Wen Jiang, Weidong Zhu, Yuhang Zhang, Qiang Xiao et al.
Advanced Functional Materials
Machine Learning in Materials Science
article

Triple‐Atom and High‐Entropy Catalysts: Advanced Architectures for Efficient Thermally Driven Organic Transformations

Wen Jiang, Weidong Zhu, Yuhang Zhang, Qiang Xiao, Huimin Zhang, Fu‐Min Zhang, Jinxia Zhu
article en

Abstract

Abstract Heterogeneous catalysis is essential for advancing sustainable chemical processes, with recent research highlighting the importance of atomic‐level precision and compositional complexity in optimizing catalytic performance. This review explores two emerging classes of catalysts—triple‐atom catalysts (TACs) and high‐entropy catalysts (HECs)—which have shown considerable promise in organic transformations. Notably, this review establishes a conceptual framework by positioning TACs as a strategic intermediate that bridges the well‐defined diatomic synergy of dual‐atom catalysts (DACs) and the configurational complexity of HECs. This unique positioning enables TACs to combine atomic precision with enhanced three‐center cooperativity, while HECs employ entropy‐driven stabilization to achieve robust catalytic activity. In conjunction with density functional theory calculations, mechanistic insights into the structure–performance relationships of these catalyst classes in representative transformations are comprehensively analyzed, with particular emphasis on their complementary roles in multistep reactions. The review first outlines the multifunctional active sites and innovative synthesis strategies of both catalyst families, emphasizing how TACs bridge the gap between DACs and HECs. Subsequently, advanced characterization techniques and the integration of machine learning approaches for rational catalyst design are discussed. Finally, based on the analysis and insights presented, future directions for development in this field are proposed.

Advanced Functional Materials
Zhejiang Normal University (CN)
Openalex Percentile: Top 67%
Machine Learning in Materials Science
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