Dynamic Workflows for Routine Materials Discovery in Surface Science

doi:10.1021/acs.jcim.8b00386

Title: Dynamic Workflows for Routine Materials Discovery in Surface Science

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Abstract

The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. In this paper, we discuss how informatics tools can accelerate research, and we present various combinations of workflows, databases, and surrogate models in the literature. This paradigm has been slower to infiltrate the catalysis community due to larger configuration spaces, difficulty in describing necessary calculations, and thermodynamic/kinetic quantities that require many interdependent calculations. We present our own informatics tool that uses dynamic dependency graphs to share, organize, and schedule calculations to enable new, flexible research workflows in surface science. This approach is illustrated for the large-scale screening of intermetallic surfaces for electrochemical catalyst activity. Similar approaches will be important to bring the benefits of informatics and data science to surface science research. Lastly, we provide our perspective on when to use these tools and considerations when creating them.

Authors:

Tran, Kevin ^[1]; Palizhati, Aini ^[1]; Back, Seoin ^[1];

^[1]

Carnegie Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering

Publication Date:: 2018-11-19

Research Org.:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC); Univ. of California, Oakland, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1543623

Grant/Contract Number:: AC02-05CH11231

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Chemical Information and Modeling

Additional Journal Information:: Journal Volume: 58; Journal Issue: 12; Journal ID: ISSN 1549-9596

Publisher:: American Chemical Society

Country of Publication:: United States

Language:: English

Subject:: Pharmacology and Pharmacy; Chemistry; Computer Science

Citation Formats


                    Tran, Kevin, Palizhati, Aini, Back, Seoin, and Ulissi, Zachary W. Dynamic Workflows for Routine Materials Discovery in Surface Science.  United States: N. p., 2018. 
        Web.  doi:10.1021/acs.jcim.8b00386.

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                    Tran, Kevin, Palizhati, Aini, Back, Seoin, & Ulissi, Zachary W. Dynamic Workflows for Routine Materials Discovery in Surface Science.  United States.  doi:10.1021/acs.jcim.8b00386.

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                    Tran, Kevin, Palizhati, Aini, Back, Seoin, and Ulissi, Zachary W. Mon .  
        "Dynamic Workflows for Routine Materials Discovery in Surface Science".  United States.  doi:10.1021/acs.jcim.8b00386.  https://www.osti.gov/servlets/purl/1543623.

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@article{osti_1543623,

  title        = {Dynamic Workflows for Routine Materials Discovery in Surface Science},

  author       = {Tran, Kevin and Palizhati, Aini and Back, Seoin and Ulissi, Zachary W.},

  abstractNote = {The rising application of informatics and data science tools for studying inorganic crystals and small molecules has revolutionized approaches to materials discovery and driven the development of accurate machine learning structure/property relationships. In this paper, we discuss how informatics tools can accelerate research, and we present various combinations of workflows, databases, and surrogate models in the literature. This paradigm has been slower to infiltrate the catalysis community due to larger configuration spaces, difficulty in describing necessary calculations, and thermodynamic/kinetic quantities that require many interdependent calculations. We present our own informatics tool that uses dynamic dependency graphs to share, organize, and schedule calculations to enable new, flexible research workflows in surface science. This approach is illustrated for the large-scale screening of intermetallic surfaces for electrochemical catalyst activity. Similar approaches will be important to bring the benefits of informatics and data science to surface science research. Lastly, we provide our perspective on when to use these tools and considerations when creating them.},

  doi          = {10.1021/acs.jcim.8b00386},

  journal      = {Journal of Chemical Information and Modeling},

  number       = 12,

  volume       = 58,

  place        = {United States},

  year         = {2018},

  month        = {11}

}

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DOI: 10.1021/acs.jcim.8b00386

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