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Title: 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:
 [1];  [2];  [3];  [1];  [4];  [2];  [1];  [2]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States). Materials Science Center
  2. National Renewable Energy Lab. (NREL), Golden, CO (United States). Computational Sciences Center
  3. National Renewable Energy Lab. (NREL), Golden, CO (United States). Materials Science Center. Computational Sciences Center
  4. 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. doi: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. doi: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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    Works referencing / citing this record:

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    • Bauers, Sage R.; Holder, Aaron; Sun, Wenhao
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    journal, October 2011

    • Paudel, Tula R.; Zakutayev, Andriy; Lany, Stephan
    • Advanced Functional Materials, Vol. 21, Issue 23
    • DOI: 10.1002/adfm.201101469

    Generation of phosphor nanoparticles for temperature sensing by laser ablation in liquid
    journal, July 2013


    Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
    journal, September 2013


    Informatics Infrastructure for the Materials Genome Initiative
    journal, July 2016


    AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
    journal, June 2012


    Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory
    journal, June 2010

    • Hautier, Geoffroy; Fischer, Christopher C.; Jain, Anubhav
    • Chemistry of Materials, Vol. 22, Issue 12
    • DOI: 10.1021/cm100795d

    Can artificial intelligence create the next wonder material?
    journal, May 2016


    Machine-learning-assisted materials discovery using failed experiments
    journal, May 2016

    • Raccuglia, Paul; Elbert, Katherine C.; Adler, Philip D. F.
    • Nature, Vol. 533, Issue 7601
    • DOI: 10.1038/nature17439

    Big–deep–smart data in imaging for guiding materials design
    journal, September 2015

    • Kalinin, Sergei V.; Sumpter, Bobby G.; Archibald, Richard K.
    • Nature Materials, Vol. 14, Issue 10
    • DOI: 10.1038/nmat4395

    A general-purpose machine learning framework for predicting properties of inorganic materials
    journal, August 2016


    Recent advances and applications of machine learning in solid-state materials science
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    • Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
    • npj Computational Materials, Vol. 5, Issue 1
    • DOI: 10.1038/s41524-019-0221-0

    Machine-learned and codified synthesis parameters of oxide materials
    journal, September 2017


    Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
    journal, July 2013

    • Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy
    • APL Materials, Vol. 1, Issue 1
    • DOI: 10.1063/1.4812323

    Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies
    journal, March 2017

    • Green, M. L.; Choi, C. L.; Hattrick-Simpers, J. R.
    • Applied Physics Reviews, Vol. 4, Issue 1
    • DOI: 10.1063/1.4977487

    Mandated data archiving greatly improves access to research data
    journal, April 2013

    • Vines, Timothy H.; Andrew, Rose L.; Bock, Dan G.
    • The FASEB Journal, Vol. 27, Issue 4
    • DOI: 10.1096/fj.12-218164

    Landolt-Börnstein, Numerical Data and Functional Relationships in Science and Technology
    journal, March 1967

    • Green, Louis C.
    • American Journal of Physics, Vol. 35, Issue 3
    • DOI: 10.1119/1.1974060

    Materials Data Science: Current Status and Future Outlook
    journal, July 2015


    Filling the gap between researchers studying different materials and different methods: a proposal for structured keywords
    journal, December 2006

    • Kajikawa, Yuya; Abe, Koji; Noda, Suguru
    • Journal of Information Science, Vol. 32, Issue 6
    • DOI: 10.1177/0165551506067125

    Shedding Light on the Dark Data in the Long Tail of Science
    journal, January 2008


    Zn–Ni–Co–O wide-band-gap p-type conductive oxides with high work functions
    journal, August 2011

    • Zakutayev, A.; Perkins, J. D.; Parilla, P. A.
    • MRS Communications, Vol. 1, Issue 1
    • DOI: 10.1557/mrc.2011.9

    Beyond bulk single crystals: A data format for all materials structure–property–processing relationships
    journal, August 2016


    Combinatorial Methods for Investigations in Polymer Materials Science
    journal, April 2002

    • Carson Meredith, J.; Karim, Alamgir; Amis, Eric J.
    • MRS Bulletin, Vol. 27, Issue 4
    • DOI: 10.1557/mrs2002.101

    Recent advances and applications of machine learning in solid-state materials science
    journal, August 2019

    • Schmidt, Jonathan; Marques, Mário R. G.; Botti, Silvana
    • npj Computational Materials, Vol. 5, Issue 1
    • DOI: 10.1038/s41524-019-0221-0

    Ternary nitride semiconductors in the rocksalt crystal structure
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    • Bauers, Sage R.; Holder, Aaron; Sun, Wenhao
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    • DOI: 10.1073/pnas.1904926116