Machine-learning-accelerated simulations to enable automatic surface reconstruction-Reference-Cited by-同舟云学术

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Machine-learning-accelerated simulations to enable automatic surface reconstruction

Published:2023-12-07 Issue:12 Volume:3 Page:1034-1044
ISSN:2662-8457
Container-title:Nature Computational Science
language:en
Short-container-title:Nat Comput Sci

Author:

Du Xiaochen^ORCID,Damewood James K.,Lunger Jaclyn R.,Millan Reisel,Yildiz Bilge,Li Lin^ORCID,Gómez-Bombarelli Rafael^ORCID

Funder

MIT | Lincoln Laboratory, Massachusetts Institute of Technology

Massachusetts Institute of Technology

National Science Foundation

U.S. Department of Defense

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Computer Science (miscellaneous)

Link

https://www.nature.com/articles/s43588-023-00571-7.pdf

Reference73 articles.

1. Shi, R., Waterhouse, G. I. & Zhang, T. Recent progress in photocatalytic CO2 reduction over perovskite oxides. Solar RRL 1, 1700126 (2017).

2. Sumaria, V., Nguyen, L., Tao, F. F. & Sautet, P. Atomic-scale mechanism of platinum catalyst restructuring under a pressure of reactant gas. J. Am. Chem. Soc. 145, 392–401 (2023).

3. Fabbri, E. et al. Dynamic surface self-reconstruction is the key of highly active perovskite nano-electrocatalysts for water splitting. Nat. Mater. 16, 925–931 (2017).

4. Zhang, Z., Wei, Z., Sautet, P. & Alexandrova, A. N. Hydrogen-induced restructuring of a Cu(100) electrode in electroreduction conditions. J. Am. Chem. Soc. 144, 19284–19293 (2022).

5. Sha, Z., Shen, Z., Cali, E., Kilner, J. A. & Skinner, S. J. Understanding surface chemical processes in perovskite oxide electrodes. J. Mater. Chem. 11, 5645–5659 (2023).

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