Prediction of Operational Lifetime of Perovskite Light Emitting Diodes by Machine Learning
Liang Zhang, Feiyue Lu, Guanhong Tao, Mengmeng Li, Zhen Yang, Airu Wang, Wei Zhu, Yu Cao, Yizheng Jin, Lin Zhu, Wei Huang, Jianpu Wang
Abstract
Perovskite light‐emitting diodes (LEDs) with advantages of high electroluminescence efficiency at high brightness, good color purity, and tunable bandgap, are believed to have potential applications in the next generation display and lighting technologies. Due to the complex degradation process, mathematic models to describe the degradation process of perovskite LEDs are absent. In this work, it is found that the mathematical fitting methods which have been widely used to describe the decay trend of organic LEDs and quantum‐dot LEDs, are unable to accurately predict the lifetime of perovskite LEDs. Then an ensemble machine learning model is developed, which utilizes data augmentation technique to predict T 50 of perovskite LEDs based on features before T 80 , achieving an accuracy of 0.995. Furthermore, the model can also accurately predict the T 90 lifetime of quantum‐dot LEDs (QLEDs) using features before T 98 , suggesting it is a useful tool to efficiently evaluate LED lifetimes.