Machine Learning Correlating Photovoltaics and Electroluminescence of Quantum Dot Light-Emitting Diodes

Thus far, no reports have been made on the correlation between photovoltaics and electroluminescence in light-emitting diodes. With machine learning assistance, we here illustrate the relationship between photovoltaics and electroluminescence of quantum dot light-emitting diodes (QLEDs) by analyzing the measurements of over 200 devices, including J–V–L, photovoltaics, and time-resolved electroluminescence (TREL) test. By applying a decision tree analysis of 17 extracted features of photovoltaics test and TREL curves, we clarify the key features of open-circuit voltage (Voc) and short-circuit current (Isc) under varied illuminated light intensities that correlate with maximum external quantum efficiency (EQEmax) of QLED devices. These photovoltaic features are discussed from the perspective of carrier injection and recombination. In addition, the exciton formation rate (r) derived from TREL curves also affects the EQEmax. The machine learning assisted methodology is also able to predict EQEmax of the QLED with a coefficient of determination of 0.78 with an artificial neural network model.