Foppiano Luca
(MaDIS, NIMS)
;
Baptista de Castro, Pedro
(MANA, NIMS)
;
Ortiz Suarez, Pedro
(Data and Web Science Group, University of Mannheim)
;
Terashima, Kensei
(MANA, NIMS)
;
Takano, Yoshihiko
(MANA, NIMS)
;
Ishii, Masashi
(MaDIS, NIMS)
Collection
Citation
Description:
(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 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.
Rights:
-
In Copyright
In Copyright
Keyword: tdm, machine learning, materials science, superconductors, database
Date published: 2022-09-15
Publisher:
Journal:
Funding:
Manuscript type: Not a journal article
MDR DOI: https://doi.org/10.48505/nims.3734
First published URL: https://hal.inria.fr/hal-03776658
Related item:
- documents
- SuperCon 2 dataset (dataset)
- https://mdr.nims.go.jp/concern/datasets/4q77fv540
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Contact agent:
Updated at: 2022-10-16 01:44:51 +0900
Published on MDR: 2022-11-10 15:27:32 +0900
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