33‐1: <i>Invited Paper:</i> TADF based OLED Devices

Yang, Joong‐Hwan; Seo, Bomin; Hong, Tae-Ryang; Baek, Jeongeun; Choi, Hyung-Jong; Noh, Hyo-Jin; Kim, Jun Yun; Yoon, Dae‐Wi; Kim, Chun Ki; Han, Chang Wook; Choi, Hyun‐Chul; Kang, In‐Byeong

doi:10.1002/sdtp.12955

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…Thermally Activated Delayed Fluorescence (TADF) material has been in the spotlight as a third-generation Organic Light-Emitting Diode (OLED) material that can be expected to have excellent internal quantum efficiency using noble-metal-free organic molecules [1,2]. Starting from conceptual development over the past decades it has continued to prosper in practical terms [3].…”

Section: Introductionmentioning

confidence: 99%

85‐3: Machine Learning Strategy Towards Inverse Design of Blue TADF Emitter: Training Excited State Properties Based on Density Functional Theory Calculations

Kim,

Lee,

Choi

et al. 2024

Symp Digest of Tech Papers

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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.

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Section: Introductionmentioning

confidence: 99%

85‐3: Machine Learning Strategy Towards Inverse Design of Blue TADF Emitter: Training Excited State Properties Based on Density Functional Theory Calculations

Kim,

Lee,

Choi

et al. 2024

Symp Digest of Tech Papers

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Correlation between ion impurity in thermally activated delayed fluorescence organic light‐emitting diode materials and device lifetime

Inoue¹,

Oyabu

Kaneko

et al. 2020

J Soc Info Display

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We propose a measurement method of ion impurity amounts of thermally activated delayed fluorescence (TADF) green dopant powders in a solution by applying a triangle voltage waveform and measuring a small displacement current that has been developed for screening and optimization of materials for thin‐film transistor‐liquid crystal displays (TFT‐LCDs). We found that there is a strong correlation between ion impurity and organic light‐emitting diode (OLED) lifetime, and we confirmed that the TADF green dopant, having a large number of ion impurity, has short OLED lifetime.

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

Yang

Choi

Bae

et al. 2021

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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.

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33‐1: Invited Paper: TADF based OLED Devices

Cited by 4 publications

References 6 publications

85‐3: Machine Learning Strategy Towards Inverse Design of Blue TADF Emitter: Training Excited State Properties Based on Density Functional Theory Calculations

85‐3: Machine Learning Strategy Towards Inverse Design of Blue TADF Emitter: Training Excited State Properties Based on Density Functional Theory Calculations

Correlation between ion impurity in thermally activated delayed fluorescence organic light‐emitting diode materials and device lifetime

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

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