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Title: 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

Abstract

Recent experiments have shown that CO reduction on oxide derived Cu nanoparticles (NP) are highly selective toward C2+ 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 C2+ product. Based on this structural criterion, we design a new periodic copper structure for CO reduction with a theoretical faradaic efficiency of 97%.

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1];  [2]; ORCiD logo [1]
+ Show Author Affiliations
  1. California Inst. of Technology (CalTech), Pasadena, CA (United States). Materials Simulation Center and Joint Center for Artificial Photosynthesis
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Oak Ridge Associated Univ., Oak Ridge, TN (United States); California Institute of Technology (CalTech), Pasadena, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1656486
Alternate Identifier(s):
OSTI ID: 1543715
Grant/Contract Number:  
AC02-05CH11231; SC0014664; SC0004993
Resource Type:
Accepted Manuscript
Journal Name:
ACS Energy Letters
Additional Journal Information:
Journal Volume: 3; Journal Issue: 12; Journal ID: ISSN 2380-8195
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; Chemistry; Electrochemistry; Energy & fuels; Science & technology; Materials science; Energy & Fuels; Science & Technology - Other Topics; Materials Science

Citation Formats

Huang, Yufeng, Chen, Yalu, Cheng, Tao, Wang, Lin-Wang, and Goddard, William A. 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. United States: N. p., 2018. Web. doi:10.1021/acsenergylett.8b01933.
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Huang, Yufeng, Chen, Yalu, Cheng, Tao, Wang, Lin-Wang, & Goddard, William A. 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. United States. https://doi.org/10.1021/acsenergylett.8b01933
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Huang, Yufeng, Chen, Yalu, Cheng, Tao, Wang, Lin-Wang, and Goddard, William A. Thu . "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". United States. https://doi.org/10.1021/acsenergylett.8b01933. https://www.osti.gov/servlets/purl/1656486.
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@article{osti_1656486,
title = {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},
author = {Huang, Yufeng and Chen, Yalu and Cheng, Tao and Wang, Lin-Wang and Goddard, William A.},
abstractNote = {Recent experiments have shown that CO reduction on oxide derived Cu nanoparticles (NP) are highly selective toward C2+ 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 C2+ product. Based on this structural criterion, we design a new periodic copper structure for CO reduction with a theoretical faradaic efficiency of 97%.},
doi = {10.1021/acsenergylett.8b01933},
journal = {ACS Energy Letters},
number = 12,
volume = 3,
place = {United States},
year = {Thu Nov 08 00:00:00 EST 2018},
month = {Thu Nov 08 00:00:00 EST 2018}
}
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Journal Article:
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https://doi.org/10.1021/acsenergylett.8b01933
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    Works referencing / citing this record:

    Atomic‐Scale Spacing between Copper Facets for the Electrochemical Reduction of Carbon Dioxide
    journal, March 2020

    • Jeong, Hyung Mo; Kwon, Youngkook; Won, Jong Ho
    • Advanced Energy Materials, Vol. 10, Issue 10
    • DOI: 10.1002/aenm.201903423

    Nanomaterials Discovery and Design through Machine Learning
    journal, January 2019

    Predicting molecular ordering in a binary liquid crystal using machine learning
    journal, August 2019

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