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Title: Dynamic Workflows for Routine Materials Discovery in Surface Science

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:
 [1];  [1];  [1]; ORCiD logo [1]
  1. Carnegie Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering
Publication Date:
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.
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.
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.
@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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