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Cited by 219

The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.

Activity Origin and Design Principles for Oxygen Reduction on Dual-Metal-Site Catalysts: A Combined Density Functional Theory and Machine Learning Study

Zhu, Xiaorong, Yan, Jiaxian, Gu, Min, Liu, Tianyang, Dai, Yafei, Gu, Yanhui, Li, Yafei·2019·The Journal of Physical Chemistry Letters

Abstract

Dual-metal-site catalysts (DMSCs) are emerging as a new frontier in the field of oxygen reduction reaction (ORR). However, there is a lack of design principles to provide a universal description of the relationship between intrinsic properties of DMSCs and the catalytic activity. Here, we identify the origin of ORR activity and unveil design principles for graphene-based DMSCs by means of density functional theory computations and machine learning (ML). Our results indicate that several experimentally unexplored DMSCs can show outstanding ORR activity surpassing that of platinum. Remarkably, our ML study reveals that the ORR activity of DMSCs is intrinsically governed by some fundamental factors, such as electron affinity, electronegativity, and radii of the embedded metal atoms. More importantly, we propose predictor equations with acceptable accuracy to quantitatively describe the ORR activity of DMSCs. Our work will accelerate the search for highly active DMSCs for ORR and other electrochemical reactions.

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Metadata

Authors
Zhu, X., Yan, J., Gu, M., Liu, T., Dai, Y., Gu, Y., & Li, Y.
Venue
The Journal of Physical Chemistry Letters
Published
2019-12-02
Volume
10
Issue
24
Pages
7760-7766
ISSN
1948-7185 (ISSN-L)
OpenAlex
W2994097301
Cited by
219
References
15
FWCI
4.79
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Activity Origin and Design Principles for Oxygen Reduction on Dual-Metal-Site Catalysts: A Combined Density Functional Theory and Machine Learning Study. https://app.cochat.ai/papers/10.1021/acs.jpclett.9b03392
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Zhu, X., Yan, J., Gu, M., Liu, T., Dai, Y., Gu, Y., & Li, Y. (2019). Activity Origin and Design Principles for Oxygen Reduction on Dual-Metal-Site Catalysts: A Combined Density Functional Theory and Machine Learning Study. The Journal of Physical Chemistry Letters. https://doi.org/10.1021/acs.jpclett.9b03392

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