Controlling aggregation-induced emission by supramolecular interactions and colloidal stability in ionic emitters for light-emitting electrochemical cells

Sanz-Velasco, Alba; Amargós-Reyes, Olivia; Kähäri, Aya; Lipinski, Sophia; Cavinato, Luca M.; Costa, Rubén D.; Kostiainen, Mauri A.; Anaya-Plaza, Eduardo

doi:10.1039/d3sc05941c

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“…Traditional experimental methods often adopt a trial-and-error approach, which demands high resources and is time-consuming to obtain high-performance AIE molecules, especially when the chemical compositions and structures are complex and diverse [24][25][26]. Quantum chemical methods such as density functional theory (DFT) can predict the wavelengths and quantum yields of molecules without chemical synthesis, but they fail to obtain AIE molecules in bulk [21,27].…”

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%

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

Bi,

Jiang,

Chen

et al. 2024

Materials

View full text Add to dashboard Cite

show abstract

Controlling aggregation-induced emission by supramolecular interactions and colloidal stability in ionic emitters for light-emitting electrochemical cells

Abstract: Chromophores face applicability limitations due to their natural tendency to aggregate, with a subsequent deactivation of their emission features. Hence, there has been a fast development of aggregation induced emission...

Cited by 1 publication

References 43 publications

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

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

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