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.
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Lab. (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:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 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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