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This content will become publicly available on July 27, 2018

Title: Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO 2 Reduction

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. Here, 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 discoveredmore » by studying facet reactivity and diversity of active sites more systematically.« less
Authors:
ORCiD logo [1] ;  [1] ;  [1] ;  [1] ;  [2] ;  [3] ;  [4] ; ORCiD logo [1] ; ORCiD logo [2] ; ORCiD logo [1] ; ORCiD logo [1] ;  [1]
  1. Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis, Dept. of Chemical Engineering; SLAC National Accelerator Lab., Menlo Park, CA (United States). SUNCAT Center for Interface Science and Catalysis
  2. Joint Center for Artificial Photosynthesis, Pasadena, CA (United States); California Inst. of Technology, Pasadena, CA (United States). Division of Chemistry and Chemical Engineering
  3. Stanford Univ., CA (United States). SUNCAT Center for Interface Science and Catalysis, Dept. of Chemical Engineering
  4. Joint Center for Artificial Photosynthesis, Pasadena, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-76SF00515; DGE-114747; AC02-05CH11231; SC0004993
Type:
Accepted Manuscript
Journal Name:
ACS Catalysis
Additional Journal Information:
Journal Volume: 7; Journal Issue: 10; Journal ID: ISSN 2155-5435
Publisher:
American Chemical Society (ACS)
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC); National Science Foundation (NSF)
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; bimetallic facets; catalysis; CO2 reduction; density functional theory; DFT; electrochemistry; energy; machine learning
OSTI Identifier:
1417634