Frontier molecular orbital weighted model based networks for revealing organic delayed fluorescence efficiency

IF 20.6 Q1 OPTICS
Zhaoming He, Hai Bi, Baoyan Liang, Zhiqiang Li, Heming Zhang, Yue Wang
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Abstract

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.

Abstract Image

基于前沿分子轨道加权模型的有机延迟荧光效率揭示网络
有机分子不含贵金属,具有较高的电致发光单位内量子效率,具有热激活延迟荧光(TADF)特性,具有替代金属基磷光材料的潜力,可作为批量生产有机发光二极管(oled)显示器的新一代发射体。在经典的化学合成和材料表征实验之外预测TADF发射体的功能仍然是一个巨大的挑战。基于深度学习(DL)的人工智能(AI)的进步为通过效率评估筛选高性能TADF材料提供了令人兴奋的机会。然而,数据驱动的材料筛选方法具有访问TADF发射体激发态特性的能力仍然非常困难,并且在很大程度上没有得到解决。受TADF分子激发态特性强烈依赖于其D-A几何和电子结构这一基本原理的启发,我们开发了用于TADF发射极筛选的电子结构注入网络(ESIN)。ESIN具有基于元素分子几何和轨道信息准确预测TADF分子的光致发光量子产率(PLQYs)的能力,并结合了基于前沿分子轨道(FMOs)权重的表示和建模特征,是一种很有前景的TADF发射效率评估和分子设计的可解释工具。
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来源期刊
Light-Science & Applications
Light-Science & Applications 数理科学, 物理学I, 光学, 凝聚态物性 II :电子结构、电学、磁学和光学性质, 无机非金属材料, 无机非金属类光电信息与功能材料, 工程与材料, 信息科学, 光学和光电子学, 光学和光电子材料, 非线性光学与量子光学
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审稿时长
2.1 months
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