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(abstract)

The automatic extraction of materials and related properties from the scientific literature is gaining attention in data-driven materials science (Materials Informatics). In this paper, we discuss Grobid-superconductors, our solution for automatically extracting superconductor material names and respective properties from text. Built as a Grobid module, it combines machine learning and heuristic approaches in a multi-step architecture that supports input data as raw text or PDF documents. Using Grobid-superconductors, we built SuperCon2, a database of 40324 materials and properties records from 37700 papers. The material (or sample) information is represented by name, chemical formula, and material class, and is characterised by shape, doping, substitution variables for components, and substrate as adjoined information. The properties include the Tc superconducting critical temperature and, when available, applied pressure with the Tc measurement method.

Data origin type: informatics_and_data_science

Rights:

• Creative Commons BY Attribution 4.0 International
  

  Creative Commons BY Attribution 4.0 International

Keyword: tdm, materials science , superconductors, machine learning, dataset

Date published: 2022-09-10

Publisher: National Institute for Materials Science

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Funding:

Manuscript type: Not a journal article

MDR DOI: https://doi.org/10.48505/nims.3735

First published URL: https://github.com/lfoppiano/supercon

Related item:

• is_documented_by
• Automatic Extraction of Materials and Properties from Superconductors Scientific Literature (dataset)
• https://mdr.nims.go.jp/concern/publications/h415pd80f

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Updated at: 2022-10-18 10:11:01 +0900

Published on MDR: 2022-11-10 15:27:13 +0900