Design of efficient blue phosphorescent bottom emitting light emitting diodes by machine learning approach

Recent advances in machine learning have allowed us to quantify the parameters that are important for the fabrication of high efficient phosphorescent bottom emitting organic light emitting diodes (PhOLEDs). Herein, we have collected 304 blue PhOLED data from the literature along with their frontier molecular orbital energy levels, triplet energies, efficiencies, device structures and layer thicknesses. Using these descriptors as the inputs and efficiency as the output, we showed that the random forest algorithm (a machine learning approach) provides significant improved predictive performance over linear regression analysis and other multivariate regression models such as extreme gradient boosting, adaptive boosting, gradient boosting and k-nearest neighbor. The triplet energy of the electron transporting layer was found to be the more critical feature. Complex correlations between various parameters on device efficiency generated by the random forest model are also presented. This study demonstrates the applicability of machine learning algorithm in extracting underlying complex correlations in blue PhOLEDs.