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

Solution-processed colloidal quantum dot light-emitting diodes (QLEDs) have received significant attention as a new route for optoelectronic applications. However, there are serious challenges to the widespread use of QLED devices. The energy-level mismatch between commonly used quantum dots (QDs) and traditional hole transport materials (HTMs) is large and significantly larger than the mismatch between the QDs and commercial electron transport materials. As a consequence, charge carriers in the light-emitting layer (EML) are imbalanced, adversely affecting the efficiency of QLED devices. Given the enormous space of organic chemistry, the design and development of novel HTMs with appropriate electronic properties is a Herculean task. Here, we apply a combined approach of active learning (AL) and high-throughput density functional theory (DFT) calculations as a novel strategy to efficiently navigate the search space in a large materials library. The AL workflow provides a systematic approach to find promising material candidates by considering multiple optoelectronic properties while keeping the load of DFT calculations low. Top candidates are further evaluated by molecular dynamics simulations and machine learning to assess their hole-transporting rates and glass-transition temperatures (Tg) of amorphous films. This work offers an efficient high-throughput materials screening strategy for QLEDs, saving the cost for excessive atomic-scale computer simulations, unnecessary materials synthesis, and failed device fabrication.