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Identifying Active Sites for CO_2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations

Chen, Yalu and Huang, Yufeng and Cheng, Tao and Goddard, William A., III (2019) Identifying Active Sites for CO_2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations. Journal of the American Chemical Society, 141 (29). pp. 11651-11657. ISSN 0002-7863. https://resolver.caltech.edu/CaltechAUTHORS:20190618-103437881

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Abstract

Gold nanoparticles (AuNPs) and dealloyed Au_3Fe core–shell NP surfaces have been shown to have dramatically improved performance in reducing CO_2 to CO (CO2RR), but the surface features responsible for these improvements are not known. The active sites cannot be identified with surface science experiments, and quantum mechanics (QM) is not practical for the 10 000 surface sites of a 10 nm NP (200 000 bulk atoms). Here, we combine machine learning, multiscale simulations, and QM to predict the performance (a-value) of all 5000–10 000 surface sites on AuNPs and dealloyed Au surfaces. We then identify the optimal active sites for CO2RR on dealloyed gold surfaces with dramatically reduced computational effort. This approach provides a powerful tool to visualize the catalytic activity of the whole surface. Comparing the a-value with descriptors from experiment, computation, or theory should provide new ways to guide the design of high-performance electrocatalysts for applications in clean energy conversion.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/jacs.9b04956DOIArticle
ORCID:
AuthorORCID
Chen, Yalu0000-0002-0589-845X
Huang, Yufeng0000-0002-0373-2210
Cheng, Tao0000-0003-4830-177X
Goddard, William A., III0000-0003-0097-5716
Alternate Title:Identifying Active Sites for CO2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations
Additional Information:© 2019 American Chemical Society. Received: May 8, 2019; Published: June 18, 2019. This work was supported by the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under Award Number DE-SC0004993. This work uses the computational resources of Caltech High Performance Computing Center (HPC). The authors declare no competing financial interest.
Group:JCAP
Funders:
Funding AgencyGrant Number
Joint Center for Artificial Photosynthesis (JCAP)UNSPECIFIED
Department of Energy (DOE)DE-SC0004993
Other Numbering System:
Other Numbering System NameOther Numbering System ID
WAG1341
Issue or Number:29
Record Number:CaltechAUTHORS:20190618-103437881
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190618-103437881
Official Citation:Identifying Active Sites for CO2 Reduction on Dealloyed Gold Surfaces by Combining Machine Learning with Multiscale Simulations. Yalu Chen, Yufeng Huang, Tao Cheng, and William A. Goddard, III. Journal of the American Chemical Society 2019 141 (29), 11651-11657. DOI: 10.1021/jacs.9b04956
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96498
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Jun 2019 19:34
Last Modified:03 Oct 2019 21:22

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