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説明:

(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.

データの性質: informatics_and_data_science

権利情報:

• Creative Commons BY Attribution 4.0 International
  Creative Commons BY Attribution 4.0 International

キーワード: tdm, materials science , superconductors, machine learning, dataset

刊行年月日: 2022-09-10

出版者: National Institute for Materials Science

掲載誌:

研究助成金:

原稿種別: 論文以外のデータ

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

公開URL: https://github.com/lfoppiano/supercon

関連資料:

• 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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更新時刻: 2022-10-18 10:11:01 +0900

MDRでの公開時刻: 2022-11-10 15:27:13 +0900