For theoretical simulations, the Materials Project has generated a large computationally derived database of electrode materials for lithium-ion batteries 13. Current data-mining research is mostly based on the datasets that are obtained from high-throughput experiments or theoretical simulations. However, a comprehensive database is essential for the data-driven discovery of new materials. This initiative led to the spin-off of many sub-projects, which have shown that data mining can be used to reduce the materials discovery timeline 9, 10, 11, 12. In 2011, the Materials Genome Initiative was launched to deploy big-data methods for the discovery of new materials 8. In recent years, the development of big-data and machine-learning methods has facilitated huge progress in chemistry and materials science, in fields such as the design and discovery of new catalysts 2, drugs 3, 4, and photovoltaic materials 5, 6, 7.
It is anticipated that data science may provide a systematic materials-by-design option that achieves this desired acceleration. Finding ways to accelerate the design and development of new materials has thus become an attractive research target. It is accepted that such methods prove frustratingly slow for the discovery of new materials. These papers are mostly generated from scientists who are reporting their current developments of new materials based on trial-and-error methods. Over the last few decades, an ever-increasing number of academic papers on battery materials have been published.
Given the increasing demand for advanced battery technologies, extensive research is being carried out in this field, especially for the development of advanced materials for safe, efficient, and high-capacity batteries.
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We also provide a Graphical User Interface (GUI) to aid the use of this database.īatteries are essential components of most electrical devices and have accordingly found widespread applications in technological areas such as portable electronics, hybrid electrical vehicles, and stationary storage devices of any size 1.
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. Public availability of these data will also enable battery materials design and prediction via data-science methods. The collected data can be used as a representative overview of battery material information that is contained within text of scientific papers. 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. 117,403 data are multivariate on a property where it is the dependent variable in part of a data series. 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.
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