Designing and screening single‐atom alloy catalysts for CO2 reduction to CH3OH via DFT and machine learning

Designing and screening single‐atom alloy catalysts for CO2 reduction to CH3OH via DFT and machine learning

AIChE Journal

10.1002/aic.18678

https://doi.org/10.1002/aic.18678

Wenyu Zhou, Haisong Feng, Shihong Zhou, Mengxin Wang, Yuping Chen, Chenyang Lu, Hao Yuan, Jing Yang, Qun Li, Luxi Tan, Lichun Dong, Yong‐Wei Zhang

10 December 2024

Zhou, W., Feng, H., Zhou, S., Wang, M., Chen, Y., Lu, C., Yuan, H., Yang, J., Li, Q., Tan, L., Dong, L., & Zhang, Y. (2024). Designing and screening single‐atom alloy catalysts for CO2 reduction to CH3OH via DFT and machine learning. AIChE Journal, 71(3). Portico. https://doi.org/10.1002/aic.18678

Carbon dioxide (CO2) 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 CO2 reduction to CH3OH. The Gibbs free energies of 477 elementary reactions across 35 SAAs involved in CO2 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 (GPDS). Three promising candidates, ZnCu, AuAg and MoNi, stand out due to their lowest GPDS among Cu‐, Ag‐ and Ni‐ hosted SAAs, respectively.

Publisher Copyright

This research / project is supported by the Agency for Science, Technology and Research, Italy-Singapore Science and Technology Cooperation - EURIPIDES
Grant Reference no. : R23I0IR040

This research / project is supported by the Agency for Science, Technology and Research - Science and Engineering Research Council - Central Research Fund
Grant Reference no. : NA

This is the peer reviewed version of the following article: Zhou, W., Feng, H., Zhou, S., Wang, M., Chen, Y., Lu, C., Yuan, H., Yang, J., Li, Q., Tan, L., Dong, L., & Zhang, Y. (2024). Designing and screening single‐atom alloy catalysts for CO2 reduction to CH3OH via DFT and machine learning. AIChE Journal, 71(3). Portico, which has been published in final form at https://doi.org/10.1002/aic.18678. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

https://oar.a-star.edu.sg/communities-collections/articles/21941

0001-1541
1547-5905

Institute of High Performance Computing