An open experimental database for exploring inorganic materials
Abstract
The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.
- Authors:
-
- National Renewable Energy Lab. (NREL), Golden, CO (United States). Materials Science Center
- National Renewable Energy Lab. (NREL), Golden, CO (United States). Computational Sciences Center
- National Renewable Energy Lab. (NREL), Golden, CO (United States). Materials Science Center. Computational Sciences Center
- National Renewable Energy Lab. (NREL), Golden, CO (United States). Materials Science Center; Computational Sciences Center
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States); Energy Frontier Research Centers (EFRC) (United States). Center for Next Generation of Materials by Design: Incorporating Metastability (CNGMD)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE); NREL Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)
- OSTI Identifier:
- 1432442
- Report Number(s):
- NREL/JA-5K00-70982
Journal ID: ISSN 2052-4463
- Grant/Contract Number:
- AC36-08GO28308
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Scientific Data
- Additional Journal Information:
- Journal Volume: 5; Journal ID: ISSN 2052-4463
- Publisher:
- Nature Publishing Group
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; 36 MATERIALS SCIENCE; applied physics; electronic devices; materials chemistry; semiconductors; solar cells
Citation Formats
Zakutayev, Andriy, Wunder, Nick, Schwarting, Marcus, Perkins, John D., White, Robert, Munch, Kristin, Tumas, William, and Phillips, Caleb. An open experimental database for exploring inorganic materials. United States: N. p., 2018.
Web. doi:10.1038/sdata.2018.53.
Zakutayev, Andriy, Wunder, Nick, Schwarting, Marcus, Perkins, John D., White, Robert, Munch, Kristin, Tumas, William, & Phillips, Caleb. An open experimental database for exploring inorganic materials. United States. https://doi.org/10.1038/sdata.2018.53
Zakutayev, Andriy, Wunder, Nick, Schwarting, Marcus, Perkins, John D., White, Robert, Munch, Kristin, Tumas, William, and Phillips, Caleb. Tue .
"An open experimental database for exploring inorganic materials". United States. https://doi.org/10.1038/sdata.2018.53. https://www.osti.gov/servlets/purl/1432442.
@article{osti_1432442,
title = {An open experimental database for exploring inorganic materials},
author = {Zakutayev, Andriy and Wunder, Nick and Schwarting, Marcus and Perkins, John D. and White, Robert and Munch, Kristin and Tumas, William and Phillips, Caleb},
abstractNote = {The use of advanced machine learning algorithms in experimental materials science is limited by the lack of sufficiently large and diverse datasets amenable to data mining. If publicly open, such data resources would also enable materials research by scientists without access to expensive experimental equipment. Here, we report on our progress towards a publicly open High Throughput Experimental Materials (HTEM) Database (htem.nrel.gov). This database currently contains 140,000 sample entries, characterized by structural (100,000), synthetic (80,000), chemical (70,000), and optoelectronic (50,000) properties of inorganic thin film materials, grouped in >4,000 sample entries across >100 materials systems; more than a half of these data are publicly available. This article shows how the HTEM database may enable scientists to explore materials by browsing web-based user interface and an application programming interface. This paper also describes a HTE approach to generating materials data, and discusses the laboratory information management system (LIMS), that underpin HTEM database. Finally, this manuscript illustrates how advanced machine learning algorithms can be adopted to materials science problems using this open data resource.},
doi = {10.1038/sdata.2018.53},
journal = {Scientific Data},
number = ,
volume = 5,
place = {United States},
year = {2018},
month = {4}
}
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