Recalibrating the Experimentally Derived Structure of the Metastable Surface Oxide on Copper via Machine Learning-Accelerated <i>In Silico</i> Global Optimization-Reference-Cited by-同舟云学术

Recalibrating the Experimentally Derived Structure of the Metastable Surface Oxide on Copper via Machine Learning-Accelerated In Silico Global Optimization

Published:2024-01-24 Issue:5 Volume:18 Page:4559-4569
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Kim Hyun Jun¹,Lee Giyeok¹,Oh Seung-Hyun Victor¹,Stampfl Catherine²³,Soon Aloysius¹²^ORCID

Affiliation:

1. Department of Materials Science & Engineering, Yonsei University, Seoul 03722, Republic of Korea

2. School of Physics, The University of Sydney, Sydney, New South Wales 2006, Australia

3. The University of Sydney Nano Institute, The University of Sydney, Sydney, New South Wales 2006, Australia

Funder

National Computational Infrastructure

National Research Foundation of Korea

National Supercomputing Center, Korea Institute of Science and Technology Information

Sydney Global Engagement Scheme: University of Sydney-Yonsei University Partnership Collaboration Award

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.3c12249

Reference73 articles.

1. Mechanistic Understanding of CO2 Reduction Reaction (CO2RR) Toward Multicarbon Products by Heterogeneous Copper-Based Catalysts

2. Plasma-Activated Copper Nanocube Catalysts for Efficient Carbon Dioxide Electroreduction to Hydrocarbons and Alcohols

3. Active and Selective Ensembles in Oxide-Derived Copper Catalysts for CO2 Reduction

4. CO2 reduction on pure Cu produces only H2 after subsurface O is depleted: Theory and experiment

5. Mechanism of CO Oxidation on Cu2 O (111) Surface: A DFT and Microkinetic Study

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