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dc.contributor.authorHuang, Shu
dc.contributor.authorCole, Jacqueline M.
dc.date.accessioned2021-02-16T16:18:33Z
dc.date.available2021-02-16T16:18:33Z
dc.date.issued2020-08-06
dc.date.submitted2020-02-24
dc.identifier.others41597-020-00602-2
dc.identifier.other602
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/317736
dc.descriptionFunder: University of Cambridge | Christ's College, University of Cambridge (Christ's College); doi: https://doi.org/10.13039/501100000590
dc.description.abstractAbstract: 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.
dc.languageen
dc.publisherNature Publishing Group UK
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectData Descriptor
dc.subject/639/638/675
dc.subject/639/766/94
dc.subject/639/301/299/891
dc.subjectdata-descriptor
dc.titleA database of battery materials auto-generated using ChemDataExtractor
dc.typeArticle
dc.date.updated2021-02-16T16:18:33Z
prism.issueIdentifier1
prism.publicationNameScientific Data
prism.volume7
dc.identifier.doi10.17863/CAM.64850
dcterms.dateAccepted2020-07-03
rioxxterms.versionofrecord10.1038/s41597-020-00602-2
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidCole, Jacqueline M. [0000-0002-1552-8743]
dc.identifier.eissn2052-4463
pubs.funder-project-idRoyal Academy of Engineering (RCSRF1819\7\10)


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's licence is described as Attribution 4.0 International (CC BY 4.0)