文章基本信息

标题：A database of battery materials auto-generated using ChemDataExtractor
本地全文：下载
作者：Shu Huang ; Jacqueline M. Cole
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2020
卷号：7
期号：1
页码：1-13
DOI：10.1038/s41597-020-00602-2
语种：English
出版社：Nature Publishing Group
摘要：A database of battery materials is presented which comprises a total of 292,313 data records, with 214,617 unique chemical-property data relations between 17,354 unique chemicals and up to five material properties: capacity, voltage, conductivity, Coulombic efficiency and energy. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. The database was auto-generated by mining text from 229,061 academic papers using the chemistry-aware natural language processing toolkit, ChemDataExtractor version 1.5, which was modified for the specific domain of batteries. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. Public availability of these data will also enable battery materials design and prediction via data-science methods. To the best of our knowledge, this is the first auto-generated database of battery materials extracted from a relatively large number of scientific papers. We also provide a Graphical User Interface (GUI) to aid the use of this database.