Back to Search Start Over

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

Authors :
Beard, Edward J.
Sivaraman, Ganesh
Vázquez-Mayagoitia, Álvaro
Vishwanath, Venkatram
Cole, Jacqueline M.
Source :
Scientific Data; 12/5/2019, Vol. 6 Issue 1, pN.PAG-N.PAG, 1p
Publication Year :
2019

Abstract

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, λ<subscript>max</subscript>, and associated extinction coefficients, ϵ, 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 λ<subscript>max</subscript> and oscillation strengths, f (related to ϵ) for a subset of validated compounds. Paired quantities of these computational and experimental data show strong correlations in λ<subscript>max</subscript>, f and ϵ, 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. Measurement(s) ultraviolet–visible spectrum • absorption wavelength • extinction coefficient Technology Type(s) digital curation Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10304897 [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20524463
Volume :
6
Issue :
1
Database :
Complementary Index
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
140159690
Full Text :
https://doi.org/10.1038/s41597-019-0306-0