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

Beard, Edward J.; Sivaraman, Ganesh; Vázquez-Mayagoitia, Álvaro; Vishwanath, Venkatram; Cole, Jacqueline M.

doi:10.1038/s41597-019-0306-0

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

Journal Article · Thu Dec 05 00:00:00 EST 2019 · Scientific Data

DOI:https://doi.org/10.1038/s41597-019-0306-0· OSTI ID:1607383

Beard, Edward J. ^[1]; ^[2]; Vázquez-Mayagoitia, Álvaro ^[2]; Vishwanath, Venkatram ^[2]; Cole, Jacqueline M. ^[3]

Univ. of Cambridge (United Kingdom); Science and Technology Facilities Council (STFC), Didcot (United Kingdom). Rutherford Appleton Lab. (RAL); DOE/OSTI
Argonne National Lab. (ANL), Lemont, IL (United States)
Univ. of Cambridge (United Kingdom); Science and Technology Facilities Council (STFC), Didcot (United Kingdom). Rutherford Appleton Lab., ISIS Neutron Source; Argonne National Lab. (ANL), Lemont, IL (United States)

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_max, 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_max 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_max, 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.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Organization:: STFC Rutherford Appleton Laboratory (RAL); Tessella; USDOE Office of Science (SC), Basic Energy Sciences (BES)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1607383

Journal Information:: Scientific Data, Journal Name: Scientific Data Journal Issue: 1 Vol. 6; ISSN 2052-4463

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Metadata record for: Comparative dataset of experimental and computational attributes of UV/vis absorption spectra Beard, Edward; Sivaraman, Ganesh; Vazquez-Mayagoitia, Alvaro figshare https://doi.org/10.6084/m9.figshare.10304897	dataset	January 2020
Metadata record for: Comparative dataset of experimental and computational attributes of UV/vis absorption spectra Beard, Edward; Sivaraman, Ganesh; Vazquez-Mayagoitia, Alvaro figshare https://doi.org/10.6084/m9.figshare.10304897.v2	dataset	January 2020
Comparative dataset of experimental and computational attributes of UV/vis absorption spectra Beard, Edward; Sivaraman, Ganesh; Vazquez-Mayagoitia, Alvaro figshare https://doi.org/10.6084/m9.figshare.7619672.v2	dataset	January 2019
Auto-generated materials database of Curie and Néel temperatures via semi-supervised relationship extraction [Supplemental Data] Court, Callum James; Cole, Jacqueline M. figshare-Supplementary information for journal article at DOI: 10.1038/sdata.2018.111, 1 ZIP file (7.18 MB) https://doi.org/10.6084/m9.figshare.c.3954418.v1	dataset	June 2018

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