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.
A feature of OLEDs that has to date received little attention is the prediction of the stability of the molecules involved in the electrical and optical processes. Here, we present computational results intended to aid in the development of stable systems. We identify degradation pathways and define new strategies to guide the synthesis of stable materials for OLED applications for both phosphorescent emitters and organic host materials. The chemical reactivity of these molecules in the active layers of the devices is further complicated by the fact that, during operation, they can be either oxidized or reduced (as they localize a hole or an electron) in addition to forming both singlet and triplet excitons.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.