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

Design and Synthesis of Novel Oxime Ester Photoinitiators Augmented by Automated Machine Learning

A new, accelerated design scheme for photoinitiators based on an advanced machine learning framework is studied. Design space for photoinitiators is set by over 120 unique oxime ester compounds synthesized and measured for their photosensitivity. Then, an automated machine learning algorithm is used for rapidly identifying the best quantitative structure–property relationship (QSPR) models among hundreds that are generated, ranked, and validated in an automated fashion to predict photosensitivity. Top-performing models are highly predictive with coefficients of determination of around 0.8 for compounds that are unknown to the models. Visual interpretation of the predictive models based on atom-site contributions offers a clear and intuitive direction to design new photoinitiators. Based on the machine learning-assisted analysis, three new oxime ester compounds were pushed for synthesis and further evaluation as novel photoinitiators. Experimental validation confirms high photosensitivity in all of the newly synthesized candidates. The work demonstrates the value of combining synthesis with the automated machine learning framework as a fast and reliable measure, which provides unbiased insights often hidden in high-dimensional data space