Deep Learning Prediction of Triplet-Triplet Annihilation Parameters in Blue Fluorescent Organic Light-Emitting Diodes
© 2024 Wiley‐VCH GmbH.
| Publié dans: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 28 vom: 07. Juli, Seite e2312774 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2024
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| Accès à la collection: | Advanced materials (Deerfield Beach, Fla.) |
| Sujets: | Journal Article deep learning exciton dynamics multilayer perceptron organic light‐emitting diodes triplet–triplet annihilation (TTA) ratio |
| Résumé: | © 2024 Wiley‐VCH GmbH. The triplet-triplet annihilation (TTA) ratio and the rate coefficient (kTT) of TTA are key factors in estimating the contribution of triplet excitons to radiative singlet excitons in fluorescent TTA organic light-emitting diodes. In this study, deep learning models are implemented to predict key factors from transient electroluminescence (trEL) data using new numerical equations. A new TTA model is developed that considers both polaron and exciton dynamics, enabling the distinction between prompt and delayed singlet decays with a fundamental understanding of the mechanism. In addition, deep learning models for predicting the kinetic coefficients and TTA ratio are established. After comprehensive optimization inspired by photophysics, determination coefficient values of 0.992 and 0.999 are achieved in the prediction of kTT and TTA ratio, respectively, indicating a nearly perfect prediction. The contribution of each kinetic parameter of polaron and exciton dynamics to the trEL curve is discussed using various deep-learning models |
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| Description: | Date Revised 12.07.2024 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.202312774 |