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Title: Research data infrastructure for high-throughput experimental materials science

Abstract

The High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.

Authors:
; ; ; ; ; ; ; ; ORCiD logo; ; ORCiD logo
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program
OSTI Identifier:
1829939
Alternate Identifier(s):
OSTI ID: 1832858
Report Number(s):
NREL/JA-5K00-80850
Journal ID: ISSN 2666-3899; S266638992100235X; 100373; PII: S266638992100235X
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Published Article
Journal Name:
Patterns
Additional Journal Information:
Journal Name: Patterns Journal Volume: 2 Journal Issue: 12; Journal ID: ISSN 2666-3899
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; data; experimental; high-throughput; materials; metadata; workflow

Citation Formats

Talley, Kevin R., White, Robert, Wunder, Nick, Eash, Matthew, Schwarting, Marcus, Evenson, Dave, Perkins, John D., Tumas, William, Munch, Kristin, Phillips, Caleb, and Zakutayev, Andriy. Research data infrastructure for high-throughput experimental materials science. United States: N. p., 2021. Web. doi:10.1016/j.patter.2021.100373.
Talley, Kevin R., White, Robert, Wunder, Nick, Eash, Matthew, Schwarting, Marcus, Evenson, Dave, Perkins, John D., Tumas, William, Munch, Kristin, Phillips, Caleb, & Zakutayev, Andriy. Research data infrastructure for high-throughput experimental materials science. United States. https://doi.org/10.1016/j.patter.2021.100373
Talley, Kevin R., White, Robert, Wunder, Nick, Eash, Matthew, Schwarting, Marcus, Evenson, Dave, Perkins, John D., Tumas, William, Munch, Kristin, Phillips, Caleb, and Zakutayev, Andriy. Wed . "Research data infrastructure for high-throughput experimental materials science". United States. https://doi.org/10.1016/j.patter.2021.100373.
@article{osti_1829939,
title = {Research data infrastructure for high-throughput experimental materials science},
author = {Talley, Kevin R. and White, Robert and Wunder, Nick and Eash, Matthew and Schwarting, Marcus and Evenson, Dave and Perkins, John D. and Tumas, William and Munch, Kristin and Phillips, Caleb and Zakutayev, Andriy},
abstractNote = {The High-Throughput Experimental Materials Database (HTEM-DB, htem.nrel.gov) is a repository of inorganic thin-film materials data collected during combinatorial experiments at the National Renewable Energy Laboratory (NREL). This data asset is enabled by NREL's Research Data Infrastructure (RDI), a set of custom data tools that collect, process, and store experimental data and metadata. Here, we describe the experimental data flow from the RDI to the HTEM-DB to illustrate the strategies and best practices currently used for materials data at NREL. Integration of the data tools with experimental instruments establishes a data communication pipeline between experimental researchers and data scientists. This work motivates the creation of similar workflows at other institutions to aggregate valuable data and increase their usefulness for future machine learning studies. In turn, such data-driven studies can greatly accelerate the pace of discovery and design in the materials science domain.},
doi = {10.1016/j.patter.2021.100373},
journal = {Patterns},
number = 12,
volume = 2,
place = {United States},
year = {Wed Dec 01 00:00:00 EST 2021},
month = {Wed Dec 01 00:00:00 EST 2021}
}

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