25-4: Methodology and Correlation of AI-Based Design for OLED Materials

AI based design for OLED materials are being tried in a variety of ways. An exemplary system is being developed to predict optical characteristics through machine learning (ML) with existing data. Once the performance descriptor is well defined and the quantum chemical calculation method is established, AI‐reverse design is expected to be possible. However, not all OLED emitting materials are equally capable of it. Different approaches are needed because the luminescence mechanism and its complexity of calculation are different depending on the material types. For pure fluorescence or even high efficiency phosphorescence, their luminescence mechanisms are relatively well defined and nearly irreversible and so the correlation between the calculation and performance could be better. If so, the reverse design is becoming possible and already it has begun to be tried a lot. However, in the case of TADF, the radiation and non‐radiation paths vary, ISC‐RISC is more reversible, and the controversy over luminescence mechanism remains. As a result, the calculating method of luminous efficiency has not yet been fully established. In this study, we want to report the consistency level of predicting characteristics of OLED materials using AI, and also discuss the difference between each emitting material types for reverse design. In particular, we also want to share the issues of calculating methods for TADF performance.