High-Throughput Screening of Hole Transport Materials for Quantum Dot Light-Emitting Diodes

Solution-processedcolloidal quantum dot light-emitting diodes(QLEDs) have received significant attention as a new route for optoelectronicapplications. However, there are serious challenges to the widespreaduse of QLED devices. The energy-level mismatch between commonly usedquantum dots (QDs) and traditional hole transport materials (HTMs)is large and significantly larger than the mismatch between the QDsand commercial electron transport materials. As a consequence, chargecarriers in the light-emitting layer (EML) are imbalanced, adverselyaffecting the efficiency of QLED devices. Given the enormous spaceof organic chemistry, the design and development of novel HTMs withappropriate electronic properties is a Herculean task. Here, we applya combined approach of active learning (AL) and high-throughput densityfunctional theory (DFT) calculations as a novel strategy to efficientlynavigate the search space in a large materials library. The AL workflowprovides a systematic approach to find promising material candidatesby considering multiple optoelectronic properties while keeping theload of DFT calculations low. Top candidates are further evaluatedby molecular dynamics simulations and machine learning to assess theirhole-transporting rates and glass-transition temperatures (T (g)) of amorphous films. This work offers an efficienthigh-throughput materials screening strategy for QLEDs, saving thecost for excessive atomic-scale computer simulations, unnecessarymaterials synthesis, and failed device fabrication.