Auto-Generating Material and Device Databases on Batteries and Solar Cells for Data-Driven Materials Discovery

This talk will introduce the ‘chemistry aware’ natural processing tool, ChemDataExtractor,[1,2] and illustrate its ability to auto-generate databases on battery and photovoltaic materials and device information. The battery database contains a total of ¼ million data records and includes chemical names of anodes, cathodes and electrolytes as well as five device properties.[3] The photovoltaics database contains ½ million data records and includes chemical names and device properties of perovskite-based and dye-sensitized solar cells.[4] The database auto-generation methods are discussed as is the applicability of these large databases to machine-learning and algorithmic pipelines which enable a design-to-device approach to data-driven materials discovery.[5,6]<br/><b>References</b><br/>[1] Swain and Cole, <i>J. Chem. Inf. Model</i>. 2016, 56, 10, 1894–1904 www.chemdataextractor.org<br/>[2] Mavracic, Court, Isazawa, Elliott, Cole, <i>J. Chem. Inf. Model</i>. 2021, 61, 9, 4280–4289 www.chemdataextractor2.org<br/>[3] Huang, Cole, <i>Sci. Data</i> (Springer Nature), 2020, 7, 260 https://doi.org/10.1038/s41597-020-00602-2<br/>[4] Beard, Cole, <i>Sci. Data</i> (Springer Nature), 2021, submitted.<br/>[5] Cole, <i>Acc. Chem. Res</i>. 2020, 53, 599-610.<br/>[6] Cooper, Beard, Vazquez-Mayagoitia, Stan, Stenning, Nye, Vigil, Tomar, Jia, Bodedla, Chen, Gallego, Franco, Carella, Thomas, Xue, Zhu, Cole<b>,</b> <i>Advanced Energy Materials</i>, (2018) 1802820 DOI: 10.1002/aenm.201802820.