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.