Kim Jae-Min, Lee Kyung Hyung, Lee Jun Yeob
School of Chemical Engineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea.
SKKU Advanced Institute of Nano Technology, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of korea.
Adv Mater. 2023 Apr;35(14):e2209953. doi: 10.1002/adma.202209953. Epub 2023 Feb 26.
Direct exploring the electroluminescence (EL) of organic light-emitting diodes (OLEDs) is a challenge due to the complicated processes of polarons, excitons, and their interactions. This study demonstrated the extraction of the polaron dynamics from transient EL by predicting the recombination coefficient via artificial intelligence, overcoming multivariable kinetics problems. The performance of a machine learning (ML) model trained by various EL decay curves is significantly improved using a novel featurization method and input node optimization, achieving an R value of 0.947. The optimized ML model successfully predicts the recombination coefficients of actual OLEDs based on an exciplex-forming cohost, enabling the quantitative understanding of the overall polaron behavior under various electrical excitation conditions.
由于极化子、激子及其相互作用的过程复杂，直接探究有机发光二极管（OLED）的电致发光（EL）具有挑战性。本研究通过人工智能预测复合系数，从瞬态EL中提取极化子动力学，克服了多变量动力学问题。使用一种新颖的特征化方法和输入节点优化，显著提高了由各种EL衰减曲线训练的机器学习（ML）模型的性能，R值达到0.947。优化后的ML模型成功预测了基于激基复合物形成共主体的实际OLED的复合系数，可以定量了解各种电激发条件下极化子的整体行为。
