Extracting Polaron Recombination from Electroluminescence in Organic Light‐Emitting Diodes by Artificial Intelligence

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 R2 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. Extraction of polaron dynamics from EL in organic light‐emitting diodes is realized by AI. The quantitative understanding of polaron dynamics in complicated light‐emitting processes is facilitated by predicting the recombination coefficient.