Prediction of Operational Lifetime of Perovskite Light Emitting Diodes by Machine Learning

• Published in: Advanced intelligent systems Vol. 6; no. 6
• Main Authors: Zhang, Liang, Lu, Feiyue, Tao, Guanhong, Li, Mengmeng, Yang, Zhen, Wang, Airu, Zhu, Wei, Cao, Yu, Jin, Yizheng, Zhu, Lin, Huang, Wei, Wang, Jianpu
• Format: Journal Article
• Language: English
• Published: Wiley 01.06.2024
• Subjects: data augmentation; machine learning; perovskite light‐emitting diodes; stability prediction
• ISSN: 2640-4567; 2640-4567
• DOI: 10.1002/aisy.202300772

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 T50 of perovskite LEDs based on features before T80, achieving an accuracy of 0.995. Furthermore, the model can also accurately predict the T90 lifetime of quantum‐dot LEDs (QLEDs) using features before T98, suggesting it is a useful tool to efficiently evaluate LED lifetimes. This work finds that the mathematical fitting methods are unable to accurately predict the lifetime of perovskite light‐emitting diodes (LEDs). Data augmentation and ensemble learning techniques are then utilized to achieve precise prediction of perovskite LED half‐life, which significantly reduces the duration of stability testing, providing technical support for achieving highly stable LEDs.