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Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO_2 Reduction

Ulissi, Zachary W. and Tang, Michael T. and Xiao, Jianping and Liu, Xinyan and Torelli, Daniel A. and Karamad, Mohammadreza and Cummins, Kyle and Hahn, Christopher and Lewis, Nathan S. and Jaramillo, Thomas F. and Chan, Karen and Norskov, Jens K. (2017) Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO_2 Reduction. ACS Catalysis, 7 (10). pp. 6600-6608. ISSN 2155-5435. https://resolver.caltech.edu/CaltechAUTHORS:20170728-081606737

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Abstract

Bimetallic catalysts are promising for the most difficult thermal and electrochemical reactions, but modeling the many diverse active sites on polycrystalline samples is an open challenge. We present a general framework for addressing this complexity in a systematic and predictive fashion. Active sites for every stable low-index facet of a bimetallic crystal are enumerated and cataloged, yielding hundreds of possible active sites. The activity of these sites is explored in parallel using a neural-network-based surrogate model to share information between the many density functional theory (DFT) relaxations, resulting in activity estimates with an order of magnitude fewer explicit DFT calculations. Sites with interesting activity were found and provide targets for follow-up calculations. This process was applied to the electrochemical reduction of CO_2 on nickel gallium bimetallics and indicated that most facets had similar activity to Ni surfaces, but a few exposed Ni sites with a very favorable on-top CO configuration. This motif emerged naturally from the predictive modeling and represents a class of intermetallic CO_2 reduction catalysts. These sites rationalize recent experimental reports of nickel gallium activity and why previous materials screens missed this exciting material. Most importantly these methods suggest that bimetallic catalysts will be discovered by studying facet reactivity and diversity of active sites more systematically.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1021/acscatal.7b01648DOIArticle
http://pubs.acs.org/doi/10.1021/acscatal.7b01648PublisherArticle
http://pubs.acs.org/doi/suppl/10.1021/acscatal.7b01648PublisherSupporting Information
ORCID:
AuthorORCID
Ulissi, Zachary W.0000-0002-9401-4918
Xiao, Jianping0000-0003-1779-6140
Torelli, Daniel A.0000-0002-6222-817X
Hahn, Christopher0000-0002-2772-6341
Lewis, Nathan S.0000-0001-5245-0538
Jaramillo, Thomas F.0000-0001-9900-0622
Chan, Karen0000-0002-6897-1108
Additional Information:© 2017 American Chemical Society. Received: May 20, 2017; Revised: July 17, 2017; Published: July 27, 2017. This material is based upon work performed 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 research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. D.A.T. and M.T acknowledge graduate fellowships through the National Science Foundation Graduate Research Fellowship under Grant No. DGE-114747. The authors declare no competing financial interests.
Group:JCAP
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0004993
Department of Energy (DOE)DE-AC02-05CH11231
NSF Graduate Research FellowshipDGE-114747
Subject Keywords:density functional theory, bimetallic facets, machine learning, catalysis, electrochemistry, CO2 reduction, machine learning, DFT, energy
Issue or Number:10
Record Number:CaltechAUTHORS:20170728-081606737
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170728-081606737
Official Citation:Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction Zachary W. Ulissi, Michael T. Tang, Jianping Xiao, Xinyan Liu, Daniel A. Torelli, Mohammadreza Karamad, Kyle Cummins, Christopher Hahn, Nathan S. Lewis, Thomas F. Jaramillo, Karen Chan, and Jens K. Nørskov ACS Catalysis 2017 7 (10), 6600-6608 DOI: 10.1021/acscatal.7b01648
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79526
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Jul 2017 16:08
Last Modified:09 Mar 2020 13:19

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