QLEDs have emerged as an alternative for optoelectronic applications. However, for widespread application of QLEDs, the device efficiency is required to be improved. There is a significant energy level mismatch between the valence band of commonly used quantum dots (QDs) and the HOMO level of traditional hole transport materials (HTMs). Given the small energy level mismatch between the conduction bands of the QDs and commercial electron transport materials, charge carriers in the light-emitting layer are imbalanced. Such a charge imbalance decreases the efficiency of QLED devices, and thus it is of great importance to design novel HTL materials with small energy mismatch with the QDs. Given the numerous potential molecules in the organic space, employing expensive and time-consuming approaches based on chemical intuition and trial-and-error experimentation is practically ineffective. Thus, realizing next-generation QLEDs technologies requires a paradigm change in materials design and development. Here, we combine active learning (AL) and high-throughput quantum mechanical calculations as a novel strategy to efficiently navigate the search space in a large materials library. The AL enables a systematic material screening by accounting multiple optoelectronic properties while minimizing the number of calculations. We further evaluated the top candidates using atomistic simulations and machine learning to investigate charge mobility and thermal stability in their amorphous films. This work offers guidelines for efficient computational screening of materials for QLEDs, reducing laborious, time-consuming, and expensive computer simulations, materials synthesis, and device fabrication.
The complex nature of the emissive layers makes it difficult to gain a fundamental understanding of the host-matrix effects on the luminescence properties of the emitters. Here, we present a computational workflow to investigate the impact of molecular packing configurations on electronic transitions in emitters. This workflow provides a framework for the systematic development and application of OLED materials. The results of this study highlight the significant impact of host–emitter interactions on radiative and nonradiative recombination processes and offer guidelines to tune these interactions for advancing OLED devices.
In this work, we describe an atomistic-scale modeling and simulation scheme to virtually screen both host materials and light emitters used in OLEDs while assessing molecular orientations in film. The work also demonstrates the ability to predict wavelength-dependent refractive indices from atomistic-scale up to achieve this goal. These findings would provide valuable guidelines for the development of new material architectures with superior optical loss properties as well as improved outcoupling efficiencies at the device level.
Development and characterization of novel OLED materials by traditional computational approaches are challenging owing to the complex factors that simultaneously influence the device performance. In this work, we will provide an overview of generative OLED materials discovery using the latest deep neural network formalism, and show an illustrative example to design novel OLED hole-transport materials. The outcome of the work will demonstrate the value of systematic and fundamental understanding of structure-property correlations that can lead to rational design of smart OLEDs with higher efficiency.
To date, the development of organic light-emitting diode (OLED) materials has been primarily based on a combination of chemical intuition and trial-and-error experimentation. The approach is often expensive and time-consuming, let alone in most instances fails to offer new materials leading to higher efficiencies. Data-driven approaches have emerged as a powerful tool to accelerate the design and discovery of novel materials with multifunctional properties for next generation OLED technologies. Virtual high-throughput methods assisted by machine learning (ML) enable a broad screening of chemical space to predict material properties and suggest new candidates for OLEDs. In order to build reliable predictive ML models for OLED materials, it is required to create and manage a high volume of data which not only maintain high accuracy but also properly assess the complexity of materials chemistry in the OLED space. Active learning (AL) is among several strategies developed to face the challenge in both materials science and life science applications, where the data management in large-scale becomes a main bottleneck. Here, we present a workflow that efficiently combines AL with atomic-scale simulations to reliably predict optoelectronic properties of OLED materials. This study provides a robust and validated framework to account for multiple parameters that simultaneously influence OLED performance. Results of this work pave the way for a fundamental understanding of optoelectronic performance of emergent layers from a molecular perspective, and further screen candidate materials with superior efficiencies before laborious simulations, synthesis, and device fabrication.
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