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

Abstract

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.

Authors:
 [1];  [2]
  1. Univ. of Cambridge, Cambridge (United Kingdom)
  2. Univ. of Cambridge, Cambridge (United Kingdom); STFC Rutherford Appleton Lab., Oxfordshire (United Kingdom); Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
Engineering and Physical Sciences Research Council (EPSRC); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
OSTI Identifier:
1460724
Grant/Contract Number:  
AC02-06CH11357
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:
36 MATERIALS SCIENCE

Citation Formats

Court, Callum J., and Cole, Jacqueline M. Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction. United States: N. p., 2018. Web. doi:10.1038/sdata.2018.111.
Court, Callum J., & Cole, Jacqueline M. Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction. United States. doi:10.1038/sdata.2018.111.
Court, Callum J., and Cole, Jacqueline M. Tue . "Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction". United States. doi:10.1038/sdata.2018.111. https://www.osti.gov/servlets/purl/1460724.
@article{osti_1460724,
title = {Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction},
author = {Court, Callum J. and Cole, Jacqueline M.},
abstractNote = {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.},
doi = {10.1038/sdata.2018.111},
journal = {Scientific Data},
number = ,
volume = 5,
place = {United States},
year = {2018},
month = {6}
}

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