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Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency
by
Liang, Baoyan
, Li, Zhiqiang
, Wang, Yue
, Bi, Hai
, Zhang, Heming
, He, Zhaoming
in
119/118
/ 639/624/399
/ 639/766
/ Artificial intelligence
/ Deep learning
/ Efficiency
/ Fluorescence
/ Lasers
/ Microwaves
/ Neural networks
/ Optical and Electronic Materials
/ Optical Devices
/ Optics
/ Organic light emitting diodes
/ Phosphorescence
/ Photonics
/ Photons
/ Physics
/ Physics and Astronomy
/ RF and Optical Engineering
2025
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Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency
by
Liang, Baoyan
, Li, Zhiqiang
, Wang, Yue
, Bi, Hai
, Zhang, Heming
, He, Zhaoming
in
119/118
/ 639/624/399
/ 639/766
/ Artificial intelligence
/ Deep learning
/ Efficiency
/ Fluorescence
/ Lasers
/ Microwaves
/ Neural networks
/ Optical and Electronic Materials
/ Optical Devices
/ Optics
/ Organic light emitting diodes
/ Phosphorescence
/ Photonics
/ Photons
/ Physics
/ Physics and Astronomy
/ RF and Optical Engineering
2025
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency
by
Liang, Baoyan
, Li, Zhiqiang
, Wang, Yue
, Bi, Hai
, Zhang, Heming
, He, Zhaoming
in
119/118
/ 639/624/399
/ 639/766
/ Artificial intelligence
/ Deep learning
/ Efficiency
/ Fluorescence
/ Lasers
/ Microwaves
/ Neural networks
/ Optical and Electronic Materials
/ Optical Devices
/ Optics
/ Organic light emitting diodes
/ Phosphorescence
/ Photonics
/ Photons
/ Physics
/ Physics and Astronomy
/ RF and Optical Engineering
2025
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Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency
Journal Article
Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency
2025
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Overview
Free of noble-metal and high in unit internal quantum efficiency of electroluminescence, organic molecules with thermally activated delayed fluorescence (TADF) features pose the potential to substitute metal-based phosphorescence materials and serve as the new-generation emitters for the mass production of organic light emitting diodes (OLEDs) display. Predicting the function of TADF emitters beyond classic chemical synthesis and material characterization experiments remains a great challenge. The advances in deep learning (DL) based artificial intelligence (AI) offer an exciting opportunity for screening high-performance TADF materials through efficiency evaluation. However, data-driven material screening approaches with the capacity to access the excited state properties of TADF emitters remain extremely difficult and largely unaddressed. Inspired by the fundamental principle that the excited state properties of TADF molecules are strongly dependent on their D-A geometric and electronic structures, we developed the Electronic Structure-Infused Network (ESIN) for TADF emitter screening. Designed with capacities of accurate prediction of the photoluminescence quantum yields (PLQYs) of TADF molecules based on elemental molecular geometry and orbital information and integrated with frontier molecular orbitals (FMOs) weight-based representation and modeling features, ESIN is a promising interpretable tool for emission efficiency evaluation and molecular design of TADF emitters.
An electronic structure-infused deep-learning neural network based on frontier molecular orbitals representation is constructed to predict the emission efficiencies of thermally activated delayed fluorescence (TADF) emitters.
Publisher
Nature Publishing Group UK,Springer Nature B.V,Nature Publishing Group
Subject
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