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.
| Original language | English |
|---|---|
| Article number | 180053 |
| Journal | Scientific data |
| Volume | 5 |
| DOIs | |
| Publication status | Published - Apr 3 2018 |
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ASJC Scopus subject areas
- Statistics and Probability
- Information Systems
- Education
- Computer Science Applications
- Statistics, Probability and Uncertainty
- Library and Information Sciences
Cite this
An open experimental database for exploring inorganic materials. / Zakutayev, Andriy; Wunder, Nick; Schwarting, Marcus; Perkins, John D.; White, Robert; Munch, Kristin; Tumas, William; Phillips, Caleb.
In: Scientific data, Vol. 5, 180053, 03.04.2018.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - An open experimental database for exploring inorganic materials
AU - Zakutayev, Andriy
AU - Wunder, Nick
AU - Schwarting, Marcus
AU - Perkins, John D.
AU - White, Robert
AU - Munch, Kristin
AU - Tumas, William
AU - Phillips, Caleb
PY - 2018/4/3
Y1 - 2018/4/3
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85044965649&partnerID=8YFLogxK
U2 - 10.1038/sdata.2018.53
DO - 10.1038/sdata.2018.53
M3 - Article
C2 - 29611842
AN - SCOPUS:85044965649
VL - 5
JO - Scientific data
JF - Scientific data
SN - 2052-4463
M1 - 180053
ER -