<scp>Designing</scp> and screening single‐atom alloy catalysts for <scp>CO<sub>2</sub></scp> reduction to <scp>CH<sub>3</sub>OH</scp> via <scp>DFT</scp> and machine learning
Abstract Carbon dioxide (CO 2 ) utilization technology is of great significance for achieving carbon neutrality, in which the catalytic materials play crucial roles, and among them, single‐atom alloys (SAAs) are of particular interests. In this study, density functional theory (DFT) calculations and machine learning are employed to assess the effectiveness of Cu‐, Ag‐, and Ni‐host SAAs as catalysts for electrochemical CO 2 reduction to CH 3 OH. The Gibbs free energies of 477 elementary reactions across 35 SAAs involved in CO 2 reduction are calculated, and by utilizing this dataset, a trained gradient boosting regression model is established with an excellent accuracy. Subsequently, the properties of 46 unknown SAAs are predicted, including their pathways, products, potential‐determining steps (PDS), and corresponding Gibbs free energies of the PDS ( G PDS ). Three promising candidates, ZnCu, AuAg and MoNi, stand out due to their lowest G PDS among Cu‐, Ag‐ and Ni‐ hosted SAAs, respectively.
Authors
- Luxi Tan (ORCID: https://orcid.org/0000-0003-0587-4607)
- Haisong Feng (ORCID: https://orcid.org/0000-0002-8461-4872)
- Lichun Dong (ORCID: https://orcid.org/0000-0002-9876-0133)
- Chenyang Lu (ORCID: https://orcid.org/0000-0002-3996-1867)
- Wenyu Zhou (ORCID: https://orcid.org/0000-0003-1671-2144)
- Jing Yang (ORCID: https://orcid.org/0000-0001-5851-2816)
- Hao Yuan (ORCID: https://orcid.org/0000-0003-3269-3326)
- Qun Li (ORCID: https://orcid.org/0000-0002-7284-1904)
- Yuping Chen (ORCID: https://orcid.org/0000-0002-4810-1922)
- Yong‐Wei Zhang (ORCID: https://orcid.org/0000-0001-7255-1678)
- Mengxin Wang (ORCID: https://orcid.org/0009-0003-1004-8067)
- Shihong Zhou (ORCID: https://orcid.org/0000-0002-0723-0293)
Institutions
- Agency for Science, Technology and Research (SG)
- Chongqing University (CN)
- Institute of High Performance Computing (SG)
- Beijing University of Chemical Technology (CN)
Publication Details
- Journal
- AIChE Journal
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1002/aic.18678
- Citations
- 5
- Primary Topic
- CO2 Reduction Techniques and Catalysts
- Type
- article
- Field-Weighted Citation Impact
- 0.51
Funders
- National Natural Science Foundation of China
- Chongqing Science and Technology Commission
- China Scholarship Council
- Centre Scientifique et Technique du Bâtiment
- Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission
- National Supercomputer Centre, Linköpings Universitet