Quantum-Chemical Simulation of Multiresonance Thermally Activated Delayed Fluorescence Materials Based on B,N-Heteroarenes Using Graph Neural Networks

Multiresonance thermally activated delayed fluorescence (MR-TADF) emitters are crucial for the next generation of electroluminescent devices due to their high efficiency and narrowband emission. In this study, we developed a simple molecular design for MR-TADF materials based on a π-extended DABNA core decorated with four different framework types (carbazole (X = none), acridine (X = C(Me)2), phenoxazine (X = O), and phenothiazine (X = S)) and further modified with 18 different annulated systems. The optoelectronic properties of these compounds were modeled using density functional theory. Based on quantum chemical calculations, an accelerated search tool for MR-TADF emitters was developed using deep learning methods, enabling the prediction of energy values approximating experimental results.多共振热激活延迟荧光（Multiresonance thermally activated delayed fluorescence, MR-TADF）发光体凭借其优异的发光效率与窄带发射特性，成为下一代电致发光器件的关键材料。本研究以π扩展的DABNA母核为基础，开发了一种简易的MR-TADF材料分子设计方案：该母核修饰有四种不同骨架类型（咔唑（X=无）、吖啶（X=C(Me)₂）、吩噁嗪（X=O）以及吩噻嗪（X=S）），并进一步通过18种不同的稠合环系进行改性。研究采用密度泛函理论对这类化合物的光电性质开展建模分析，基于量子化学计算结果，借助深度学习方法开发了一款MR-TADF发光体快速筛选工具，该工具可预测与实验结果高度接近的能级数值。