Chen, Yalu and Huang, Yufeng and Cheng, Tao and Goddard, William A., III (2019) Identifying Active Sites for CO₂ 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. doi:10.1021/jacs.9b04956. 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.
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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 | ||||||||||
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Issue or Number: | 29 | ||||||||||
DOI: | 10.1021/jacs.9b04956 | ||||||||||
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: | 16 Nov 2021 17:21 |
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