Machine Learning-Assisted Design of Dual-Atom Catalysts and the "Convergence-Selective Transfer" Charge Regulation Mechanism for ORR

Cheng He; Hongxing Yan; Xi Shen; Wenxue Zhang

doi:10.1039/d6ta03340g

January 1, 2026Journal of Materials Chemistry A

Machine Learning-Assisted Design of Dual-Atom Catalysts and the "Convergence-Selective Transfer" Charge Regulation Mechanism for ORR

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Key Points

This research aims to enhance the understanding of charge regulation mechanisms in dual-atom catalysts for the oxygen reduction reaction (ORR).
Utilized machine learning algorithms for the design of dual-atom catalysts.
Assessed the catalytic mechanisms involved in the ORR using various screening techniques.
Evaluated the environmental adaptability of these catalysts under different conditions.
Identified a new 'convergence-selective transfer' charge regulation mechanism that improves reaction kinetics.
Demonstrated enhanced catalytic performance in designed dual-atom catalysts compared to traditional models.

Implication

Investigates charge regulation in dual-atom catalysts, highlighting its potential for improving fuel cell efficiency.

Abstract

The oxygen reduction reaction (ORR) is a crucial cathodic process in fuel cells, but the unclear catalytic mechanism, low screening efficiency, and poor environmental adaptability led to slow reaction kinetics,...

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Cite This Study

He et al. (2026) studied this question.

synapsesocial.com/papers/6a3631a1db0793dc1a538687 https://doi.org/10.1039/d6ta03340g

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Machine Learning-Assisted Design of Dual-Atom Catalysts and the "Convergence-Selective Transfer" Charge Regulation Mechanism for ORR

Key Points

Implication

Abstract

Discussion

Cite This Study

Machine Learning-Assisted Design of Dual-Atom Catalysts and the "Convergence-Selective Transfer" Charge Regulation Mechanism for ORR

Key Points

Implication

Abstract

Discussion

Cite This Study

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