Automatic extraction of materials and properties from superconductors scientific literature

Autor:	Foppiano, Luca, de Castro, Pedro Baptista, Suarez, Pedro Ortiz, Terashima, Kensei, Takano, Yoshihiko, Ishii, Masashi
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language Condensed Matter - Superconductivity Computer Science - Machine Learning
Zdroj:	STAM:M, 2023, VOL. 3, NO. 1, 2153633
Druh dokumentu:	Working Paper
DOI:	10.1080/27660400.2022.2153633
Popis:	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. Comment: 20 pages, 11 figures, 8 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.15600 Zobrazit plný text záznamu View this record from Arxiv