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A database of battery materials auto-generated using ChemDataExtractor

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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… Click to show full abstract

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. Measurement(s) battery capacity • Voltage • electrical conductivity • Faraday efficiency • energy • Chemical Properties Technology Type(s) digital curation • computational modeling technique Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12646277

Keywords: battery materials; auto generated; battery; database; database battery

Journal Title: Scientific Data
Year Published: 2020

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