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Description:

Data-driven material research for property prediction and material design using machine learning methods requires a large quantity, wide variety, and high-quality materials data. For battery materials, which are commonly polycrystalline, ceramics, and composites, multiscale data on substances, materials, and batteries are required. In this work, we develop a data network composed of three interlinked databases, from which we can obtain comprehensive data on substances such as crystal structures and electronic structures, data on materials such as chemical composition, structure, and properties, and data on batteries such as battery composition, operation conditions, and capacity. The data are extracted from research papers on solid electrolytes and cathode materials, selected by screening more than 330 thousand papers using natural language processing tools. Data extraction and curation are carried out by editors specialized in material science and trained in data standardization.

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• Creative Commons BY Attribution 4.0 International
  

Keyword: Material databases, battery material, crystal structure, ionic conductivity, nature language processing, cathode, solid electrolyte

Date published: 2024-12-31

Publisher: Informa UK Limited

Journal:

• Science and Technology of Advanced Materials (ISSN: 14686996) vol. 25 issue. 1 2403328

Funding:

• Japan Science and Technology Agency “Advanced Battery Collaboration” project of CO
• National Institute for Materials Science NIMS Materials Open Platform for All Solid-State B
• National Institute for Materials Science Research Network and Facility Services Division

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1080/14686996.2024.2403328

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Updated at: 2024-10-04 08:30:19 +0900

Published on MDR: 2024-10-04 08:30:19 +0900