Screening Multicomponent Alloys Using Evidence Theory and High-Throughput DFT Calculations for C2-Selective eCO2RR

In this paper, we propose a strategy for screening multicomponent alloys using evidence theory and high-throughput density functional theory calculations. We demonstrate an electrochemical C2-selective CO2 reduction reaction (eCO2RR) on multicomponent alloys as a practical study. The proposed strategy can screen potential C2-selective alloys from enormous element combinations without complicated structural descriptors or a complete data set. Therefore, this strategy is expected to effectively design multicomponent alloys with vast element combinations.

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Publication Details

Journal
The Journal of Physical Chemistry C
Published
2024-10-07
DOI
https://doi.org/10.1021/acs.jpcc.4c04180
Citations
3
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.35

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article

Screening Multicomponent Alloys Using Evidence Theory and High-Throughput DFT Calculations for C2-Selective eCO2RR

Fumiaki Kuroda, Minoru Otani, Masaya Ibe
3 citations
The Journal of Physical Chemistry C
Machine Learning in Materials Science
0.35
article

Screening Multicomponent Alloys Using Evidence Theory and High-Throughput DFT Calculations for C2-Selective eCO2RR

Fumiaki Kuroda, Minoru Otani, Masaya Ibe
article en
3 citations

Abstract

In this paper, we propose a strategy for screening multicomponent alloys using evidence theory and high-throughput density functional theory calculations. We demonstrate an electrochemical C2-selective CO2 reduction reaction (eCO2RR) on multicomponent alloys as a practical study. The proposed strategy can screen potential C2-selective alloys from enormous element combinations without complicated structural descriptors or a complete data set. Therefore, this strategy is expected to effectively design multicomponent alloys with vast element combinations.

The Journal of Physical Chemistry CVol. 128(41)
University of Tsukuba (JP)
Ministry of Education, Culture, Sports, Science and Technology, Japan Society for the Promotion of Science
Openalex Percentile: Top 44%
Machine Learning in Materials Science
0.35
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