广东季华实验室Wang Yue研究组在最新研究中，报道了基于分子轨道加权模型的有机延迟荧光效率揭示网络。相关论文发表在2025年2月10日出版的《光：科学与应用》杂志上。

据了解，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，要实现具有访问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分子激发态性质强烈依赖于，其D-A几何和电子结构的基本原理的启发，该研究组开发了用于TADF发射极筛选的电子结构-主题网络（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。

附：英文原文

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

Author: He, Zhaoming, Bi, Hai, Liang, Baoyan, Li, Zhiqiang, Zhang, Heming, Wang, Yue

Issue&Volume: 2025-02-10

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.

DOI: 10.1038/s41377-024-01713-w

Source: https://www.nature.com/articles/s41377-024-01713-w