Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction

Court, Callum J.; Cole, Jacqueline M.

doi:10.1038/sdata.2018.111

Title: Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction

Journal Article · Tue Jun 19 00:00:00 EDT 2018 · Scientific Data

DOI:https://doi.org/10.1038/sdata.2018.111· OSTI ID:1460724

Court, Callum J. ^[1]; Cole, Jacqueline M. ^[2]

Univ. of Cambridge, Cambridge (United Kingdom)
Univ. of Cambridge, Cambridge (United Kingdom); STFC Rutherford Appleton Lab., Oxfordshire (United Kingdom); Argonne National Lab. (ANL), Argonne, IL (United States)

article presents an auto-generated database of 39,822 records containing chemical compounds and their associated Curie and Néel magnetic phase transition temperatures. The database was produced using natural language processing and semi-supervised quaternary relationship extraction, applied to a corpus of 68,078 chemistry and physics articles. Evaluation of the database shows an estimated overall precision of 73%. Therein, records processed with the text-mining toolkit, ChemDataExtractor, were assisted by a modified Snowball algorithm, whose original binary relationship extraction capabilities were extended to quaternary relationship extraction. Consequently, its machine learning component can now train with ≤500 seeds, rather than the 4,000 originally used. Data processed with the modified Snowball algorithm affords 82% precision. Database records are available in MongoDB, CSV and JSON formats which can easily be read using Python, R, Java and MatLab. As a result, this makes the database easy to query for tackling big-data materials science initiatives and provides a basis for magnetic materials discovery.

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Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: Engineering and Physical Sciences Research Council (EPSRC); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1460724

Journal Information:: Scientific Data, Vol. 5; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 53 works

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References (15)

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Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction [Supplemental Data] Court, Callum James; Cole, Jacqueline M. figshare-Supplementary information for journal article at DOI: 10.1038/sdata.2018.111, 1 ZIP file (7.18 MB) https://doi.org/10.6084/m9.figshare.c.3954418	dataset	June 2018
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A reference set of curated biomedical data and metadata from clinical case reports Caufield, J. Harry; Zhou, Yijiang; Garlid, Anders O. Scientific Data, Vol. 5, Issue 1 https://doi.org/10.1038/sdata.2018.258	journal	November 2018
A picture is worth a thousand words: applying natural language processing tools for creating a quantum materials database map Venugopal, Vineeth; Broderick, Scott R.; Rajan, Krishna MRS Communications, Vol. 9, Issue 4 https://doi.org/10.1557/mrc.2019.136	journal	October 2019
Growing field of materials informatics: databases and artificial intelligence Lopez-Bezanilla, Alejandro; Littlewood, Peter B. MRS Communications, Vol. 10, Issue 1 https://doi.org/10.1557/mrc.2020.2	journal	January 2020

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