Dual‐ Sabatier Optima: How Reaction Mechanism Determines Activity Volcano Map of Dual‐Atom Catalysts for Oxygen Reduction Reaction

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Authors: Jin Liu, Hao Li, Haoxiang Xu, Daojian Cheng
Cited by: 1
References: 58

Abstract

ABSTRACT Dual‐atom catalysts (DACs) have demonstrated superior potential in the oxygen reduction reaction (ORR). However, the single‐peak activity volcano derived from classical associative mechanism is contrast to the large‐scale experimental data from the Digital Catalysis Platform ( DigCat ). Herein, we studied ORR over 200 DACs from thermodynamic and kinetic perspectives, and found that the dissociative mechanism is generally dominant for DACs. By integrating potential‐related microkinetic modeling and machine learning (ML)‐derived interpretable structural descriptors, we discovered a dual‐ Sabatier optima volcano map against Δ G (OH*) (or structural descriptors), which was rigorously validated against available experimental data. Dual‐ Sabatier optima stem from the rate‐determining step of dissociative mechanism switching among three elementary reactions (O 2 dissociation → 2OH protonation → OH protonation), which can be extended across DACs containing transition metal, metal‐like, and non‐metal elements as center atoms. It opens a brand‐new perspective for rational design of DACs and atomically dispersed catalysts for other reactions beyond ORR, of which the dominant reaction mechanism may be different from single‐atom catalysts (SACs) and lead to diverse activity volcano maps. Most importantly, this work illustrates that new phenomenon can be identified from “old experimental data” under a large data scale, with the help of theoretical simulations integrated with interpretable ML .

Fields of Study & Subjects

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

Publication details

Journal / source
Angewandte Chemie
Publisher
Wiley
Volume
138
Issue
24
Publication date
27 April 2026
Language
EN