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, Jens K. Nørskov

Research output: Contribution to journalArticle

99 Citations (Scopus)

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 CO2 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 CO2 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.

Original languageEnglish
Pages (from-to)6600-6608
Number of pages9
JournalACS Catalysis
Volume7
Issue number10
DOIs
Publication statusPublished - Jan 1 2017

Keywords

  • Bimetallic facets
  • CO reduction
  • Catalysis
  • DFT
  • Density functional theory
  • Electrochemistry
  • Energy
  • Machine learning
  • Machine learning

ASJC Scopus subject areas

  • Catalysis
  • Chemistry(all)

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  • Cite this

    Ulissi, Z. W., Tang, M. T., Xiao, J., Liu, X., Torelli, D. A., Karamad, M., Cummins, K., Hahn, C., Lewis, N. S., Jaramillo, T. F., Chan, K., & Nørskov, J. K. (2017). Machine-learning methods enable exhaustive searches for active Bimetallic facets and reveal active site motifs for CO2 reduction. ACS Catalysis, 7(10), 6600-6608. https://doi.org/10.1021/acscatal.7b01648