Scientific Data · 2019 · 110 citations · 25 references
Natural SciencesChemistryPhysicsComputational ChemistryComputational Materials ScienceSpectroscopyAb Initio MethodsDensity Functional TheoryMaterials InformaticsOptical Properties Of MaterialsOptical SpectroscopyUltraviolet-visible SpectroscopyAbsorption SpectraData-driven Materials DiscoveryElectronic Property PredictionOptical AbsorptionOptical Absorption SpectraOptical PropertiesSpectral Property PredictionUltraviolet-visible Absorption Spectroscopy
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 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.
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