A comprehensive data network for data-driven study of battery materials
| Autor: | Yibin Xu, Yen-Ju Wu, Huiping Li, Lei Fang, Shigenobu Hayashi, Ayako Oishi, Natsuko Shimizu, Riccarda Caputo, Pierre Villars |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Science and Technology of Advanced Materials, Vol 25, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 14686996 1878-5514 1468-6996 |
| DOI: | 10.1080/14686996.2024.2403328 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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