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A database of battery materials auto-generated using ChemDataExtractor

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Huang, Shu 
Cole, Jacqueline M.  ORCID logo


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.


Funder: University of Cambridge | Christ's College, University of Cambridge (Christ's College); doi:


Data Descriptor, /639/638/675, /639/766/94, /639/301/299/891, data-descriptor

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Nature Publishing Group UK
Royal Academy of Engineering (RCSRF1819\7\10)