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

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:

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)

Publication Date:: Tue Jun 19 00:00:00 EDT 2018

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.

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                    Court, Callum J., & Cole, Jacqueline M. Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction.  United States.  https://doi.org/10.1038/sdata.2018.111

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                    Court, Callum J., and Cole, Jacqueline M. Tue .  
"Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction".  United States.  https://doi.org/10.1038/sdata.2018.111.  https://www.osti.gov/servlets/purl/1460724.

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@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         = {Tue Jun 19 00:00:00 EDT 2018},

  month        = {Tue Jun 19 00:00:00 EDT 2018}

}

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https://doi.org/10.1038/sdata.2018.111

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