Screening of single-atom catalysts for CO<sub>2</sub> electroreduction to CH<sub>4</sub> using DFT calculations and machine learning

An enhanced high-throughput DFT screening model additionally incorporates *OH reduction to refine selectivity, identifying 9 CO 2 RR-active TM@C 5 N SACs. Machine learning reveals key descriptors, guiding catalyst design.

Authors

Jingzhuo Zhou (ORCID: https://orcid.org/0000-0002-0633-9991)
Li Shi (ORCID: https://orcid.org/0000-0002-2265-1037)
Yixin Ouyang (ORCID: https://orcid.org/0000-0002-1555-1396)
Xiaobing Wang (ORCID: https://orcid.org/0000-0003-2687-4817)
Xiuyun Zhang
Zhengyu Gu

Institutions

Nanjing University of Posts and Telecommunications (CN)
Ministry of Education (BD)
Yangzhou University (CN)

Publication Details

Journal: Journal of Materials Chemistry A
Published: 2025-01-01
DOI: https://doi.org/10.1039/d5ta03431k
Citations: 9
Primary Topic: Machine Learning in Materials Science
Type: article
Field-Weighted Citation Impact: 4.66

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article

Screening of single-atom catalysts for CO₂ electroreduction to CH₄ using DFT calculations and machine learning

Jingzhuo Zhou, Li Shi, Yixin Ouyang, Xiaobing Wang et al.

Jan 1, 2025

9 citations

Journal of Materials Chemistry A

Machine Learning in Materials Science

4.66

article

Screening of single-atom catalysts for CO₂ electroreduction to CH₄ using DFT calculations and machine learning

Jingzhuo Zhou, Li Shi, Yixin Ouyang, Xiaobing Wang, Xiuyun Zhang, Zhengyu Gu

January 1, 2025article en

9 citations

Abstract

An enhanced high-throughput DFT screening model additionally incorporates *OH reduction to refine selectivity, identifying 9 CO 2 RR-active TM@C 5 N SACs. Machine learning reveals key descriptors, guiding catalyst design.

Journal of Materials Chemistry AVol. 13(29)

Nanjing University of Posts and Telecommunications (CN), Ministry of Education (BD), Yangzhou University (CN)

Responsible consumption and production

Openalex Percentile: Top 5%

Machine Learning in Materials Science

4.66

DOI Source OpenAlex

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