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 Laboratory (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 = {Tue Apr 03 00:00:00 EDT 2018},
month = {Tue Apr 03 00:00:00 EDT 2018}
}
Free Publicly Available Full Text
Publisher's Version of Record
Other availability
Search WorldCat to find libraries that may hold this journal
Cited by: 90 works
Citation information provided by
Web of Science
Web of Science
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.
Works referenced in this record:
Doping Rules and Doping Prototypes in A2BO4 Spinel Oxides
journal, October 2011
- Paudel, Tula R.; Zakutayev, Andriy; Lany, Stephan
- Advanced Functional Materials, Vol. 21, Issue 23
Cation off-stoichiometry leads to high -type conductivity and enhanced transparency in Co ZnO and Co NiO thin films
journal, February 2012
- Zakutayev, A.; Paudel, T. R.; Ndione, P. F.
- Physical Review B, Vol. 85, Issue 8
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
Chemically sensitive parallel analysis of combinatorial catalyst libraries
journal, June 2001
- Snively, Christopher M.; Oskarsdottir, Gudbjorg; Lauterbach, Jochen
- Catalysis Today, Vol. 67, Issue 4
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
Can artificial intelligence create the next wonder material?
journal, May 2016
- Nosengo, Nicola
- Nature, Vol. 533, Issue 7601
Generation of phosphor nanoparticles for temperature sensing by laser ablation in liquid
journal, July 2013
- Avanesyan, Sergey M.; Haglund, Richard F.
- Applied Physics A, Vol. 113, Issue 1
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
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
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
Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
journal, September 2013
- Saal, James E.; Kirklin, Scott; Aykol, Muratahan
- JOM, Vol. 65, Issue 11
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012
- Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong
- Computational Materials Science, Vol. 58
New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design
journal, May 2002
- Belsky, Alec; Hellenbrandt, Mariette; Karen, Vicky Lynn
- Acta Crystallographica Section B Structural Science, Vol. 58, Issue 3
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
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
Materials Data Science: Current Status and Future Outlook
journal, July 2015
- Kalidindi, Surya R.; De Graef, Marc
- Annual Review of Materials Research, Vol. 45, Issue 1
Shedding Light on the Dark Data in the Long Tail of Science
journal, January 2008
- P. Bryan Heidorn,
- Library Trends, Vol. 57, Issue 2
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
Machine-learned and codified synthesis parameters of oxide materials
journal, September 2017
- Kim, Edward; Huang, Kevin; Tomala, Alex
- Scientific Data, Vol. 4, Issue 1
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
Inorganic Materials Database for Exploring the Nature of Material
journal, November 2011
- Xu, Yibin; Yamazaki, Masayoshi; Villars, Pierre
- Japanese Journal of Applied Physics, Vol. 50, Issue 11S
A general-purpose machine learning framework for predicting properties of inorganic materials
journal, August 2016
- Ward, Logan; Agrawal, Ankit; Choudhary, Alok
- npj Computational Materials, Vol. 2, Issue 1
Informatics Infrastructure for the Materials Genome Initiative
journal, July 2016
- Dima, Alden; Bhaskarla, Sunil; Becker, Chandler
- JOM, Vol. 68, Issue 8
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
Inverse design approach to hole doping in ternary oxides: Enhancing -type conductivity in cobalt oxide spinels
journal, November 2011
- Perkins, J. D.; Paudel, T. R.; Zakutayev, A.
- Physical Review B, Vol. 84, Issue 20
Beyond bulk single crystals: A data format for all materials structure–property–processing relationships
journal, August 2016
- Michel, Kyle; Meredig, Bryce
- MRS Bulletin, Vol. 41, Issue 08
Combinatorial screening for new materials in unconstrained composition space with machine learning
journal, March 2014
- Meredig, B.; Agrawal, A.; Kirklin, S.
- Physical Review B, Vol. 89, Issue 9
High Throughput Experimental Materials Database
dataset, January 2017
- Zakutayev, Andriy; Perkins, John; Schwarting, Marcus
- National Renewable Energy Laboratory - Data (NREL-DATA), Golden, CO (United States); National Renewable Energy Laboratory
Inorganic Materials Database for Exploring the Nature of Material
journal, November 2011
- Xu, Yibin; Yamazaki, Masayoshi; Villars, Pierre
- Japanese Journal of Applied Physics, Vol. 50, Issue 11
A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
text, January 2016
- Ward, Logan; Agrawal, Ankit; Choudhary, Alok
- arXiv
AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations
journal, June 2012
- Curtarolo, Stefano; Setyawan, Wahyu; Wang, Shidong
- Computational Materials Science, Vol. 58
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
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
Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials
journal, June 2013
- Green, Martin L.; Takeuchi, Ichiro; Hattrick-Simpers, Jason R.
- Journal of Applied Physics, Vol. 113, Issue 23
Shedding Light on the Dark Data in the Long Tail of Science
journal, January 2008
- P. Bryan Heidorn,
- Library Trends, Vol. 57, Issue 2
Works referencing / citing this record:
Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, September 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
- Advanced Science, Vol. 6, Issue 21
Synthesis of Lanthanum Tungsten Oxynitride Perovskite Thin Films
journal, May 2019
- Talley, Kevin R.; Mangum, John; Perkins, Craig L.
- Advanced Electronic Materials, Vol. 5, Issue 7
A Critical Review of Machine Learning of Energy Materials
journal, January 2020
- Chen, Chi; Zuo, Yunxing; Ye, Weike
- Advanced Energy Materials, Vol. 10, Issue 8
Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods
journal, July 2019
- Ludwig, Alfred
- npj Computational Materials, Vol. 5, Issue 1
Tracking materials science data lineage to manage millions of materials experiments and analyses
journal, July 2019
- Soedarmadji, Edwin; Stein, Helge S.; Suram, Santosh K.
- npj Computational Materials, Vol. 5, Issue 1
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
Stabilization of wide band-gap p-type wurtzite MnTe thin films on amorphous substrates
journal, January 2018
- Siol, Sebastian; Han, Yanbing; Mangum, John
- Journal of Materials Chemistry C, Vol. 6, Issue 23
Descriptor–property relationships in heterogeneous catalysis: exploiting synergies between statistics and fundamental kinetic modelling
journal, January 2019
- Pirro, Laura; Mendes, Pedro S. F.; Paret, Stijn
- Catalysis Science & Technology, Vol. 9, Issue 12
Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning
journal, January 2019
- Barnard, A. S.; Opletal, G.
- Nanoscale, Vol. 11, Issue 48
Systematic exploration of the mechanical properties of 13 621 inorganic compounds
journal, January 2019
- Chibani, Siwar; Coudert, François-Xavier
- Chemical Science, Vol. 10, Issue 37
Progress and prospects for accelerating materials science with automated and autonomous workflows
journal, January 2019
- Stein, Helge S.; Gregoire, John M.
- Chemical Science, Vol. 10, Issue 42
Ternary nitride semiconductors in the rocksalt crystal structure
journal, July 2019
- Bauers, Sage R.; Holder, Aaron; Sun, Wenhao
- Proceedings of the National Academy of Sciences, Vol. 116, Issue 30
Modelling of framework materials at multiple scales: current practices and open questions
journal, May 2019
- Fraux, Guillaume; Chibani, Siwar; Coudert, François-Xavier
- Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 377, Issue 2149
Templated Growth of Metastable Polymorphs on Amorphous Substrates with Seed Layers
journal, January 2020
- Han, Yanbing; Trottier, Ryan; Siol, Sebastian
- Physical Review Applied, Vol. 13, Issue 1
Wurtzite materials in alloys of rock salt compounds
journal, January 2020
- Han, Yanbing; Millican, Samantha L.; Liu, Jun
- Journal of Materials Research, Vol. 35, Issue 8
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management
journal, June 2019
- Pendleton, Ian M.; Cattabriga, Gary; Li, Zhi
- MRS Communications, Vol. 9, Issue 3
Data-centric science for materials innovation
journal, September 2018
- Tanaka, Isao; Rajan, Krishna; Wolverton, Christopher
- MRS Bulletin, Vol. 43, Issue 9
The Material Indices Method in the Sustainable Engineering Design Process: A Review
journal, October 2019
- Branowski, Bogdan; Zabłocki, Marek; Sydor, Maciej
- Sustainability, Vol. 11, Issue 19
Data‐Driven Materials Science: Status, Challenges, and Perspectives
journal, November 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam Stuart
- Advanced Science, Vol. 7, Issue 2
Data-driven materials science: status, challenges and perspectives
text, January 2019
- Himanen, Lauri; Geurts, Amber; Foster, Adam S.
- arXiv
Systematic exploration of the mechanical properties of 13 621 inorganic compounds
journal, January 2019
- Chibani, Siwar; Coudert, François-Xavier
- Chemical Science, Vol. 10, Issue 37
COMBIgor: data analysis package for combinatorial materials science
preprint, January 2019
- Talley, Kevin R.; Bauers, Sage R.; Melamed, Celeste L.
- arXiv