Comparative dataset of experimental and computational attributes of UV/vis absorption spectra

被引:71
作者
Beard, Edward J. [1 ,2 ]
Sivaraman, Ganesh [3 ]
Vazquez-Mayagoitia, Alvaro [3 ]
Vishwanath, Venkatram [3 ]
Cole, Jacqueline M. [1 ,2 ,3 ,4 ]
机构
[1] Univ Cambridge, Dept Phys, Cavendish Lab, JJ Thomson Ave, Cambridge CB3 0HE, England
[2] STFC Rutherford Appleton Lab, ISIS Neutron & Muon Source, Harwell Sci & Innovat Campus, Didcot OX11 0QX, Oxon, England
[3] Argonne Natl Lab, 9700 South Cass Ave, Lemont, IL 60439 USA
[4] Univ Cambridge, Dept Chem Engn & Biotechnol, West Cambridge Site,Philippa Fawcett Dr, Cambridge CB3 0FS, England
关键词
OPTIMIZATION;
D O I
10.1038/s41597-019-0306-0
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The ability to auto-generate databases of optical properties holds great prospects in data-driven materials discovery for optoelectronic applications. We present a cognate set of experimental and computational data that describes key features of optical absorption spectra. This includes an auto-generated database of 18,309 records of experimentally determined UV/vis absorption maxima, lambda(max), and associated extinction coefficients, epsilon, where present. This database was produced using the text-mining toolkit, ChemDataExtractor, on 402,034 scientific documents. High-throughput electronic-structure calculations using fast (simplified Tamm-Dancoff approach) and traditional (time-dependent) density functional theory were executed to predict lambda(max) and oscillation strengths, f (related to epsilon) for a subset of validated compounds. Paired quantities of these computational and experimental data show strong correlations in lambda(max), f and epsilon, laying the path for reliable in silico calculations of additional optical properties. The total dataset of 8,488 unique compounds and a subset of 5,380 compounds with experimental and computational data, are available in MongoDB, CSV and JSON formats. These can be queried using Python, R, Java, and MATLAB, for data-driven optoelectronic materials discovery.
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页数:11
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