Function-Oriented Design of Metal Electrocatalytic Materials: From Reaction Mechanisms to Active Site Engineering

Electrocatalysis represents a core technology for the conversion and storage of renewable energy. Traditional research has relied heavily on the trial-and-error approach or simple modification of known materials, lacking a guiding framework that can systematically link reaction mechanisms with material design. To address this gap, this review aims to establish a unified, function-oriented integrated design framework. This framework adheres to the core logic of “mechanism-driven demand─function definition─site engineering”: starting from the intrinsic mechanism of the target reaction, it identifies the rate-determining steps and key intermediates; then reversely defines the electronic structure, geometric configuration, and dynamic response characteristics required of active sites; and ultimately guides the rational construction of catalytic materials. Organizing the discourse around the core functions of active sites, this paper systematically analyzes a series of important systems, ranging from hydrogen/oxygen reactions and carbon–nitrogen cycles to the electrocatalytic transformation of organic molecules. By clarifying the inherent correlations between structure, function, and performance across different systems, the review summarizes universal design principles for overcoming the performance limits of conventional materials through the synergistic optimization of multidimensional descriptors and the regulation of dynamic active states. Finally, this paper highlights several emerging research directions, including the rational programming of dynamic active sites, machine learning-assisted inverse design, and the systematic regulation of interfacial microenvironments. It is anticipated that this work will provide a structured methodological guide for advancing electrocatalytic materials from relatively fragmented empirical exploration toward more systematic, rational construction.

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Publication Details

Journal
ACS Catalysis
Published
2026-02-12
DOI
https://doi.org/10.1021/acscatal.5c08247
Primary Topic
Machine Learning in Materials Science
Type
article
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article

Function-Oriented Design of Metal Electrocatalytic Materials: From Reaction Mechanisms to Active Site Engineering

Jierui Zhou, Jiani Han, Lei Wang, Yaodong Yu et al.
ACS Catalysis
Machine Learning in Materials Science
article

Function-Oriented Design of Metal Electrocatalytic Materials: From Reaction Mechanisms to Active Site Engineering

Jierui Zhou, Jiani Han, Lei Wang, Yaodong Yu, Jianping Lai, Yanxue Chao
article en

Abstract

Electrocatalysis represents a core technology for the conversion and storage of renewable energy. Traditional research has relied heavily on the trial-and-error approach or simple modification of known materials, lacking a guiding framework that can systematically link reaction mechanisms with material design. To address this gap, this review aims to establish a unified, function-oriented integrated design framework. This framework adheres to the core logic of “mechanism-driven demand─function definition─site engineering”: starting from the intrinsic mechanism of the target reaction, it identifies the rate-determining steps and key intermediates; then reversely defines the electronic structure, geometric configuration, and dynamic response characteristics required of active sites; and ultimately guides the rational construction of catalytic materials. Organizing the discourse around the core functions of active sites, this paper systematically analyzes a series of important systems, ranging from hydrogen/oxygen reactions and carbon–nitrogen cycles to the electrocatalytic transformation of organic molecules. By clarifying the inherent correlations between structure, function, and performance across different systems, the review summarizes universal design principles for overcoming the performance limits of conventional materials through the synergistic optimization of multidimensional descriptors and the regulation of dynamic active states. Finally, this paper highlights several emerging research directions, including the rational programming of dynamic active sites, machine learning-assisted inverse design, and the systematic regulation of interfacial microenvironments. It is anticipated that this work will provide a structured methodological guide for advancing electrocatalytic materials from relatively fragmented empirical exploration toward more systematic, rational construction.

ACS CatalysisVol. 16(5)
Qingdao University of Science and Technology (CN), Environmental Protection Engineering (Greece) (GR), Ministry of Education (BD)
Affordable and clean energy
Openalex Percentile: Top 59%
Machine Learning in Materials Science
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