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

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Authors: Yanxue Chao, Jiani Han, Yaodong Yu, Jiaxuan Zhou, Jianping Lai, Lei Wang
Cited by: 2
References: 167

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.

Fields of Study & Subjects

Machine Learning in Materials Science Electrocatalysts for Energy Conversion CO2 Reduction Techniques and Catalysts

Publication details

Journal / source
ACS Catalysis
Publisher
American Chemical Society (ACS)
Volume
16
Issue
5
Pages
4153–4192
Publication date
12 February 2026
Language
EN