![]() In this study, we present a reliable and high-quality database of the optical properties of organic compounds that can be used for various purposes in diverse research fields.Ī total of 1,358 articles containing organic compounds were downloaded from journals of Nature Research, American Chemical Society, Royal Society of Chemistry, Springer, and Elsevier by exploring keywords such as fluorescence, luminescence, emission, OLED, fluorescence lifetime, or PLQY. ![]() ![]() 1b, c), are essential for the development of emitters in OLEDs, fluorescent bioimaging dyes, and fluorescent sensors. Similarly, the emission and fluorescent properties, which are characterized by the maximum emission wavelength ( λ emi, max), bandwidth ( σ emi), PLQY (Φ QY), and excited state lifetime ( τ) (Fig. 1a), which are important parameters for the design of chromophores for specific applications in various research fields such as photovoltaics, dyes, and optical filters. 1, the absorption properties of organic chromophores are characterized by the first maximum absorption wavelength ( λ abs, max), bandwidth ( σ abs), and extinction coefficient ( ε max) (Fig. However, no databases are currently available for the experimental absorption, emission, and fluorescence properties of organic chromophores.Īs illustrated in Fig. have reported the datasets of experimental and computational ultraviolet–visible (UV–Vis) absorption spectra 7. Recently, the absorption peaks and extinction coefficients of small organic molecules have already been obtained using quantum chemical calculations and have been used for machine learning 4, 5, 6. Therefore, databases on optical properties can be used to model the quantitative structure–property relationship for designing new organic chromophores with desired optical properties. The optical properties, such as absorption and emission maximum wavelengths and their bandwidths, extinction coefficient, photoluminescence quantum yield (PLQY), and lifetime, are important factors in characterizing organic chromophores. Thus, databases for specific applications need to be available or collected. However, databases are a prerequisite for data-driven sciences based on machine learning. Therefore, data-driven sciences based on machine learning have emerged as a promising alternative method and have been applied in many research areas 1, 2, 3. However, such theoretical calculations require high computational costs. Theoretical calculations based on ab initio and density functional theory methods have been extensively used to characterize the optical properties of newly designed organic chromophores. Therefore, it would be useful to reliably and quickly predict the optical properties of newly designed organic chromophores prior to their synthesis. Organic chromophores used in optoelectronics, organic light emitting diodes (OLEDs), staining, fluorescent dyes, and bioimaging dyes, have been steadily developed.
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