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 phosph...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(Al)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.nspired 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-lnfused 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)weightbased representation and modeling features,ESIN is a promising interpretable tool for emission efficiency evaluation and moleculardesign of TADF emitters.展开更多
基金supported by the National Natural Science Foundation of China(52173282 and 21935005)the National Key R&D Program of China(2020YFA0714601)+2 种基金Guangdong Basic and Applied Basic Research Foundation(No.2022A1515140078)Jihua Laboratory(X190321TF190 and X210221TP210)Foshan Science and Technology Innovation Team Special Project(1920001000128)。
文摘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(Al)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.nspired 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-lnfused 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)weightbased representation and modeling features,ESIN is a promising interpretable tool for emission efficiency evaluation and moleculardesign of TADF emitters.
