High-throughput screening of stable Ag–Pd–F catalysts for formate oxidation reaction using machine learning

The crystal graph convolutional neural networks predict stable Ag–Pd–F catalysts for the formate oxidation reaction from Materials Project data with DFT verifying their thermodynamic, dynamic stability and catalytic activity.

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Publication Details

Journal
Journal of Materials Chemistry A
Published
2025-01-01
DOI
https://doi.org/10.1039/d4ta07674e
Citations
7
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
3.62
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article

High-throughput screening of stable Ag–Pd–F catalysts for formate oxidation reaction using machine learning

Wanxuan Zhang, Peng Xu, Fuyi Chen, Xiaoqing Liu et al.
7 citations
Journal of Materials Chemistry A
Machine Learning in Materials Science
3.62
article

High-throughput screening of stable Ag–Pd–F catalysts for formate oxidation reaction using machine learning

Wanxuan Zhang, Peng Xu, Fuyi Chen, Xiaoqing Liu, Fanzhe Ma
article en
7 citations

Abstract

The crystal graph convolutional neural networks predict stable Ag–Pd–F catalysts for the formate oxidation reaction from Materials Project data with DFT verifying their thermodynamic, dynamic stability and catalytic activity.

Journal of Materials Chemistry AVol. 13(17)
Northwestern Polytechnical University (CN)
Openalex Percentile: Top 7%
Machine Learning in Materials Science
3.62
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