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

Abstract

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 (T g) 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.

Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age

“…And each property data is translated to a dimensionless value in the range of 0 and 1. [29] 2.3. Model selection ML algorithms could be briefly classified into two categories: supervised learning and unsupervised learning.…”

“…Abroshan combined ML and high-throughput quantum chemical calculations to design HTMs materials with a library of nearly 9000 molecules. [29] The ML framework is shown to successfully identify the candidates by considering the criteria for minimizing the hole reorganization energy (λ h ) and HOMO energy (E HOMO ). This work paves the way for efficient screening of materials for organic electronics with superior efficiencies before laborious synthesis and device fabrication.…”

“…For instance, to build a materials library for screening HTMs, Abroshan et al performed the structural R-group enumeration using a genetic algorithm featured in the Schrödinger Materials Science Suite, and built a library of 8627 materials. [29] In addition, during computer-assisted optoelectronic materials design, the quantitative structure/property relationships model combines experimental descriptors with those generated by DFT calculations to expand data sets, which improves the prediction of molecular properties.…”

Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery

“…Abroashan et al [43] adopted QBC to find potent candidates for quantum dot light-emitting diodes (QLED) hole-transport material. The low hole reorganization energy (λ h ) and HOMO energy (E HOMO ) were used as selection criteria.…”

Hole-Injection-Barrier Effect on the Degradation of Blue Quantum-Dot Light-Emitting Diodes

“…Due to the relatively low work functions of electrodes and the deep-lying highest occupied molecular orbitals (HOMO) of blue-emitting QDs, the injection barriers at the electrode/HTL and the HTL/QD interfaces are synergistically responsible for the inefficient hole injection. Although various electrode/HTL combinations have been attempted, a practical guiding rule for materials selection is still lacking. , The once considered feasible proposal of deep-HOMO HTLs lacks support from theory or experiment. , In addition, the exploration of the guiding rule has been limited by the understanding of energy-level alignment and injection dynamics. Due to separate sources of disorder and insulating surface ligands, the charge transport in colloidal QD assemblies is dominated by hopping .…”