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

Autor: Jacqueline M. Cole, Ganesh Sivaraman, Edward J. Beard, Venkatram Vishwanath, Álvaro Vázquez-Mayagoitia
Přispěvatelé: Sivaraman, Ganesh [0000-0001-9056-9855], Apollo - University of Cambridge Repository
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Statistics and Probability
639/638/630
Data Descriptor
Computer science
Uv vis absorption
02 engineering and technology
639/301/1019
Library and Information Sciences
010402 general chemistry
01 natural sciences
Spectral line
Education
639/638/298/398
Optoelectronic materials
Computational methods
MATLAB
lcsh:Science
computer.programming_language
639/301/1034/1037
34 Chemical Sciences
Cheminformatics
Experimental data
Python (programming language)
021001 nanoscience & nanotechnology
0104 chemical sciences
Computer Science Applications
3407 Theoretical and Computational Chemistry
Networking and Information Technology R&D (NITRD)
Optical materials
Density functional theory
lcsh:Q
Statistics
Probability and Uncertainty
0210 nano-technology
Biological system
Maxima
data-descriptor
computer
Information Systems
Materials for optics
Zdroj: Scientific Data, Vol 6, Iss 1, Pp 1-11 (2019)
Scientific Data
ISSN: 2052-4463
Popis: Funder: US Department of Energy, Office of Science, Office of Basic Energy Sciences, DE-AC02-06CH11357
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, λmax, 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 λmax 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 λmax, 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.
Databáze: OpenAIRE
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