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Identification of the Selective Sites for Electrochemical Reduction of CO to C_(2+) Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning

Huang, Yufeng and Chen, Yalu and Cheng, Tao and Wang, Lin-Wang and Goddard, William A., III (2018) Identification of the Selective Sites for Electrochemical Reduction of CO to C_(2+) Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning. ACS Energy Letters, 3 (12). pp. 2983-2988. ISSN 2380-8195. http://resolver.caltech.edu/CaltechAUTHORS:20181113-112609899

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Abstract

Recent experiments have shown that CO reduction on oxide derived Cu nanoparticles (NP) are highly selective toward C_(2+) products. However, understanding of the active sites on such NPs is limited, because the NPs have ∼200 000 atoms with more than 10 000 surface sites, far too many for direct quantum mechanical calculations and experimental identifications. We show here how to overcome the computational limitation by combining multiple levels of theoretical computations with machine learning. This approach allows us to map the machine learned CO adsorption energies on the surface of the copper nanoparticle to construct the active site visualization (ASV). Furthermore, we identify the structural criteria for optimizing selective reduction by predicting the reaction energies of the potential determining step, ΔE_(OCCOH), for the C_(2+) product. Based on this structural criterion, we design a new periodic copper structure for CO reduction with a theoretical faradaic efficiency of 97%.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/acsenergylett.8b01933DOIArticle
https://pubs.acs.org/doi/suppl/10.1021/acsenergylett.8b01933PublisherSupporting Information
ORCID:
AuthorORCID
Huang, Yufeng0000-0002-0373-2210
Cheng, Tao0000-0003-4830-177X
Goddard, William A., III0000-0003-0097-5716
Alternate Title:Identification of the Selective Sites for Electrochemical Reduction of CO to C2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning
Additional Information:© 2018 American Chemical Society. Received: October 9, 2018; Accepted: November 8, 2018; Published: November 8, 2018. 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 material is also based upon work supported by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists, Office of Science Graduate Student Research (SCGSR) program. The SCGSR program is administered by the Oak Ridge Institute for Science and Education for the DOE under contract number DE-SC0014664. This work uses the resource of National Energy Research Scientific Computing center (NERSC). The authors declare no competing financial interest.
Group:JCAP
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0004993
Department of Energy (DOE)DE‐SC0014664
Other Numbering System:
Other Numbering System NameOther Numbering System ID
WAG1308
Record Number:CaltechAUTHORS:20181113-112609899
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20181113-112609899
Official Citation:Identification of the Selective Sites for Electrochemical Reduction of CO to C2+ Products on Copper Nanoparticles by Combining Reactive Force Fields, Density Functional Theory, and Machine Learning. Yufeng Huang, Yalu Chen, Tao Cheng, Lin-Wang Wang, and William A. Goddard, III. ACS Energy Letters 2018 3 (12), 2983-2988. DOI: 10.1021/acsenergylett.8b01933
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90870
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:14 Nov 2018 15:39
Last Modified:04 Jan 2019 20:58

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