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.
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.
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.
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