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説明:

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 characterized 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.

権利情報:

• In Copyright
  In Copyright

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

刊行年月日: 2022-09-15

出版者:

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.3734

公開URL: https://hal.inria.fr/hal-03776658

関連資料:

• documents
• SuperCon 2 dataset (dataset)
• https://mdr.nims.go.jp/concern/datasets/4q77fv540

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更新時刻: 2022-10-16 01:44:51 +0900

MDRでの公開時刻: 2022-11-10 15:27:32 +0900