AI-driven advances in the design of RTP and TADF luminescent material

The design of room-temperature phosphorescence (RTP) and thermally activated delayed fluorescence (TADF) materials is crucial for advancing organic light-emitting diodes (OLEDs) and other optoelectronic devices. However, traditional experimental methods are inefficient. This review discusses the application of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in optimizing RTP and TADF materials. AI-driven approaches have revolutionized the discovery and design process by efficiently predicting material properties and performance. We highlight challenges in RTP and TADF material design, including optimizing singlet-triplet energy gaps and minimizing non-radiative decay. Additionally, we explore how ML models, combined with quantum chemical calculations, accelerate the identification of promising materials. The integration of AI allows for rapid screening and optimization of luminescent materials, improving quantum yield, fluorescence efficiency, and stability. With the rapid growth of AI applications in materials science, this review aims to provide insights and guide future research toward leveraging AI for the development of next-generation luminescent materials for OLED technologies.