Graph‐Based Representation Approach for Deep Learning of Organic Light‐Emitting Diode Devices

The performance prediction of organic light‐emitting diode (OLED) devices using artificial intelligence is significantly limited due to the lack of representational feature data. This study proposes a novel graph‐based representation methodology to effectively address these challenges. Various graph convolution methods are explored, resulting in an ideal representation of the device parameters in the static equilibrium state, which is crucial for accurate modeling. This representation not only exhibits parameter‐like characteristics but also encapsulates essential physical meanings that enhance interpretability. Additionally, the trained predictive model demonstrates relatively high accuracy, making it a reliable tool for practical applications. Finally, this research serves as a valuable initial study for predicting and designing OLED devices, paving the way for future advancements in the field.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Graph‐Based Representation Approach for Deep Learning of Organic Light‐Emitting Diode Devices ; day:29 ; month:10 ; year:2024 ; extent:11 ; Advanced intelligent systems ; (29.10.2024) (gesamt 11)

Contributors, Places and Time

• Creator: Lee, Taeyang ; Choi, Jeongwhan ; Na, Inyeob ; Yoo, Insun ; Woo, Sungil ; Kim, Kwang Jong ; Park, Mikyung ; Yang, Joonghwan ; Min, Jeongguk ; Lee, Seokwoo ; Park, Noseong ; Yang, Joonyoung

Further information

• DOI: 10.1002/aisy.202400598
• URN: urn:nbn:de:101:1-2410301305562.727571157054
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:28 AM CEST