Automatic extraction of materials and properties from superconductors scientific literature. Issue 1 (31st December 2023)

• Record Type: Journal Article
• Authors: Foppiano, Luca; Castro, Pedro Baptista; Ortiz Suarez, Pedro; Terashima, Kensei; Takano, Yoshihiko; Ishii, Masashi
• Is Part Of: Science and Technology of Advanced Materials: Methods. Volume 3:Issue 1(2023)
• Publication Date: 2023-12-31
• Subjects: Materials informatics -- superconductors -- machine learning -- NLP -- TDM
• DOI: 10.1080/27660400.2022.2153633
• Languages: English
• ISSNs: 2766-0400

Abstract

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 SuperCon 2, a database of 40, 324 materials and properties records from 37, 700 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. Graphical Abstract: uf0001