Replication Data for: Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts

Lim, Jin Soo; Molinari, Nicola; Duanmu, Kaining; Sautet, Philippe; Kozinsky, Boris

doi:10.7910/DVN/TCGFWJ

Title: Replication Data for: Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts

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Abstract

The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.

Authors:

Lim, Jin Soo; Molinari, Nicola; Duanmu, Kaining; Sautet, Philippe; Kozinsky, Boris

Harvard Univ., Cambridge, MA (United States); OSTI
Harvard Univ., Cambridge, MA (United States)
Univ. of California, Los Angeles, CA (United States)

Publication Date:: Fri Sep 27 00:00:00 EDT 2019

DOE Contract Number:: SC0012573

Research Org.:: Harvard Univ., Cambridge, MA (United States). Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC) (EFRC)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

Subject:: 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

OSTI Identifier:: 1878480

DOI:: https://doi.org/10.7910/DVN/TCGFWJ

Citation Formats


                    Lim, Jin Soo, Molinari, Nicola, Duanmu, Kaining, Sautet, Philippe, and Kozinsky, Boris. Replication Data for: Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts.  United States: N. p., 2019. 
        Web.  doi:10.7910/DVN/TCGFWJ.

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                    Lim, Jin Soo, Molinari, Nicola, Duanmu, Kaining, Sautet, Philippe, & Kozinsky, Boris. Replication Data for: Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts.  United States.  doi:https://doi.org/10.7910/DVN/TCGFWJ

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                    Lim, Jin Soo, Molinari, Nicola, Duanmu, Kaining, Sautet, Philippe, and Kozinsky, Boris. 2019.  
"Replication Data for: Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts".  United States.  doi:https://doi.org/10.7910/DVN/TCGFWJ.  https://www.osti.gov/servlets/purl/1878480. Pub date:Fri Sep 27 00:00:00 EDT 2019

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@article{osti_1878480,

  title        = {Replication Data for: Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts},

  author       = {Lim, Jin Soo and Molinari, Nicola and Duanmu, Kaining and Sautet, Philippe and Kozinsky, Boris},

  abstractNote = {The data underlying this published work have been made publicly available in this repository as part of the IMASC Data Management Plan. This work was supported as part of the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences under Award # DE-SC0012573.},

  doi          = {10.7910/DVN/TCGFWJ},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Fri Sep 27 00:00:00 EDT 2019},

  month        = {Fri Sep 27 00:00:00 EDT 2019}

}

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DOI: https://doi.org/10.7910/DVN/TCGFWJ

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Automated Detection and Characterization of Surface Restructuring Events in Bimetallic Catalysts

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Surface restructuring in bimetallic systems has recently been shown to play a crucial role in heterogeneous catalysis. In particular, the segregation in binary alloys can be reversed in the presence of strongly bound adsorbates. Mechanistic characterization of such restructuring phenomena at the atomic level remains scarce and challenging because of the large configurational space that must be explored. To this end, we propose an automated method to discover elementary surface restructuring processes in an unbiased fashion using Pd/Ag as an example. Here, we employ high-temperature classical molecular dynamics to rapidly detect restructuring events, isolate them, and optimize using density functionalmore »« less
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