Optimized Machine learning techniques Enable prediction of organic dyes photophysical Properties: Absorption Wavelengths, emission Wavelengths, and quantum yields

Mahato, Kapil Dev; Kumar, Uday

doi:10.1016/j.saa.2023.123768

Cited by 4 publications

(1 citation statement)

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“…The best models achieved up to 94% accuracy for emission color and a minimum mean average error of 25.8 nm for wavelength, facilitating the design of carbon dots with targeted optical properties [39]. Mahato et al optimized a series of ML models to predict the physical properties of organic dyes, and the derived R 2 values for absorption and emission wavelengths that were 0.7% and 0.4% larger, respectively, than those recently reported by the gradient boosted regression (GBR) models [40].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules

Bi,

Jiang,

Chen

et al. 2024

Materials

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The aggregation-induced emission (AIE) effect exhibits a significant influence on the development of luminescent materials and has made remarkable progress over the past decades. The advancement of high-performance AIE materials requires fast and accurate predictions of their photophysical properties, which is impeded by the inherent limitations of quantum chemical calculations. In this work, we present an accurate machine learning approach for the fast predictions of quantum yields and wavelengths to screen out AIE molecules. A database of about 563 organic luminescent molecules with quantum yields and wavelengths in the monomeric/aggregated states was established. Individual/combined molecular fingerprints were selected and compared elaborately to attain appropriate molecular descriptors. Different machine learning algorithms combined with favorable molecular fingerprints were further screened to achieve more accurate prediction models. The simulation results indicate that combined molecular fingerprints yield more accurate predictions in the aggregated states, and random forest and gradient boosting regression algorithms show the best predictions in quantum yields and wavelengths, respectively. Given the successful applications of machine learning in quantum yields and wavelengths, it is reasonable to anticipate that machine learning can serve as a complementary strategy to traditional experimental/theoretical methods in the investigation of aggregation-induced luminescent molecules to facilitate the discovery of luminescent materials.

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Section: Introductionmentioning

confidence: 99%