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Title: An open experimental database for exploring inorganic materials

Journal Article · · Scientific Data
 [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
+ Show Author Affiliations

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.

Research Organization:
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 Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE); NREL Laboratory Directed Research and Development (LDRD) Program; USDOE Office of Science (SC)
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1432442
Report Number(s):
NREL/JA-5K00-70982
Journal Information:
Scientific Data, Vol. 5; ISSN 2052-4463
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 90 works
Citation information provided by
Web of Science

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High Throughput Experimental Materials Database
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Cited By (21)

Data‐Driven Materials Science: Status, Challenges, and Perspectives journal September 2019
Synthesis of Lanthanum Tungsten Oxynitride Perovskite Thin Films journal May 2019
A Critical Review of Machine Learning of Energy Materials journal January 2020
Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods journal July 2019
Tracking materials science data lineage to manage millions of materials experiments and analyses journal July 2019
Recent advances and applications of machine learning in solid-state materials science journal August 2019
Stabilization of wide band-gap p-type wurtzite MnTe thin films on amorphous substrates journal January 2018
Descriptor–property relationships in heterogeneous catalysis: exploiting synergies between statistics and fundamental kinetic modelling journal January 2019
Predicting structure/property relationships in multi-dimensional nanoparticle data using t-distributed stochastic neighbour embedding and machine learning journal January 2019
Systematic exploration of the mechanical properties of 13 621 inorganic compounds journal January 2019
Progress and prospects for accelerating materials science with automated and autonomous workflows journal January 2019
Ternary nitride semiconductors in the rocksalt crystal structure journal July 2019
Modelling of framework materials at multiple scales: current practices and open questions
  • Fraux, Guillaume; Chibani, Siwar; Coudert, François-Xavier
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 377, Issue 2149 https://doi.org/10.1098/rsta.2018.0220
journal May 2019
Templated Growth of Metastable Polymorphs on Amorphous Substrates with Seed Layers journal January 2020
Wurtzite materials in alloys of rock salt compounds journal January 2020
Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management journal June 2019
Data-centric science for materials innovation journal September 2018
The Material Indices Method in the Sustainable Engineering Design Process: A Review journal October 2019
Data‐Driven Materials Science: Status, Challenges, and Perspectives journal November 2019
Data-driven materials science: status, challenges and perspectives text January 2019
COMBIgor: data analysis package for combinatorial materials science preprint January 2019

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