A Machine-Learning Study on the Prediction of the Efficiency of Red CdSe Quantum Dot Light Emitting Diodes

Abstract

Quantum dot light emitting diodes (QLEDs) with a structure of ITO (indium tin oxide)/poly (3,4-ethylenedioxythiophene) polystyrene sulfonate/hole transport layer/QD (quantum dot)/electron transport layer/Al using CdSe QD were fabricated, and machine learning was used to predict the efficiency of the QLEDs. A machine learning model was constructed to relate the efficiency of the QLEDs and the electronic transport properties of the QLEDs using a large number of data generated by device simulation. The efficiency limiting factors found by the machine learning model are consistent with those found experimentally. In addition, the machine learning model predicts the electronic properties of the hole transport layer for the fabrication of high-efficiency CdSe QLEDs.