We introduce inverse design strategy utilizing machine learning (ML) models to discover efficient blue thermally activated delayed fluorescence (TADF) organic emitter materials. Here, we leverage graph neural network (GNN) to predict the characteristic intrinsic materials properties of TADF such as excited state energy levels and their transition properties. The GNN model is trained based on density‐functional theory (DFT) calculation results to meet the TADF properties. We discuss consistency between experimental observation and ML predictions, and examined conditions for improving the accuracy of DFT calculations and ML models on top of it.

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.

AbstractWe have implemented a 240Hz 55‐inch ultra definition (UD, or 3840times2160) resolution TV panel using amorphous IGZO TFT. As the resolution and the frame rate of a panel increase, pixel charging time and panel transmittance ratio decrease. Among various data driving architectures, the data single 1G1D driving renders cost benefit, process competitiveness and design flexibility. To apply this driving architecture to a high resolution, high mobility of TFT and low RC delay are required. We suggest a‐IGZO TFT and copper metallization technology as a solution.

We have implemented a 240Hz 55-inch ultra definition (UD, or 3840x2160) resolution TV panel using amorphous IGZO TFT. Among various data driving architectures, the data single 1G1D driving renders cost benefit, process competitiveness and design flexibility. So we suggest a-IGZO TFT and copper metallization technology as a solution.

A 2×2 module prototype is developed with four 15‐inch RGBW LCDs. Transmittance of LCD is increased with the low resolution, that is, qXGA(512×RG×384×BW). Using a new RGBW conversion algorithm, white and R/G/B luminance are increased 270% and 130% respectively in comparison with the 15‐inch conventional RGB LCD. An electronics system is implemented and a GUI is developed to support the hardware image control for tiled display.