Perovskite- and Dye-Sensitized Solar-Cell Device Databases Auto-generated Using ChemDataExtractor

The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where...

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Vydáno v:Scientific data Ročník 9; číslo 1; s. 329 - 19
Hlavní autoři: Beard, Edward J., Cole, Jacqueline M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 17.06.2022
Nature Publishing Group
Nature Portfolio
Témata:
639/301/299/946
639/4077/4072/4062
639/638/630
639/638/675
639/766/259
cheminformatics
Data Descriptor
devices for energy harvesting
Dyes
energy
Humanities and Social Sciences
information theory and computation
multidisciplinary
Photovoltaic cells
Science
Science (multidisciplinary)
solar cells
SOLAR ENERGY
ISSN:2052-4463, 2052-4463
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Shrnutí:The number of scientific publications reporting cutting-edge third-generation photovoltaic devices is increasing rapidly, owing to the pressing need to develop renewable-energy technologies that address the climate-change crisis. Consequently, the field could benefit from a central repository where photovoltaic-performance metrics, such as the power-conversion efficiency ( η ) are recorded. We present two automatically generated databases that contain photovoltaic properties and device material data for dye-sensitized solar cells (DSCs) and perovskite solar cells (PSCs), totalling 660,881 data entries representing 57,678 photovoltaic devices. The databases were generated by applying the text-mining toolkit ChemDataExtractor on a corpus of 25,720 articles. A multi-faceted evaluation, incorporating manual and automatic methods, was applied to ensure that the data contained therein were of the highest quality, with precision metrics ranging from 73.1% to 95.8%. The DSC database contains 475,045 entries representing 41,680 devices, and the PSC database contains 185,836 entries representing 15,818 devices. The databases are available in MongoDB and JSON formats, which can be queried in Python, R, Java and MATLAB for data-driven photovoltaic materials discovery. Measurement(s) photovoltaic device parameters Technology Type(s) natural language processing
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AC02-06CH11357
USDOE Office of Science (SC), Basic Energy Sciences (BES)
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-022-01355-w