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.
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.
As OLED applications increase, so do the demands on properties of the component materials, active layers and devices. The development of flexible OLEDs, a popular future OLED application, require better understanding and control of the mechanical properties of OLED materials and interaction with polymer substrates. Fabrication costs, use of extended classes of materials and the need for large surface area applications drives interest in solution-phase processing techniques; requiring OLEDs with different solubilities and glass transition temperatures than traditional vacuum deposited layers and device stacks. In this era of designing for multiple property requirements, computational techniques can provide important capability to screen new materials and understand the relationship between chemical structure and dependent properties. In this work we show automated molecular dynamics (MD) simulation workflows that efficiently and accurately calculate mechanical and physical properties of OLED materials.
Design and development of highly efficient organic and organometallic dopants is one of the central challenges in the organic light-emitting diodes (OLEDs) technology. Recent advances in the computational materials science have made it possible to apply computer-aided evaluation and screening framework directly to the design space of organic lightemitting diodes (OLEDs). In this work, we will showcase two major components of the latest in silico framework for development of organometallic phosphorescent dopants – (1) rapid screening of dopants by machine-learned quantum mechanical models and (2) phosphorescence lifetime predictions with spin-orbit coupled calculations (SOC-TDDFT). The combined work of virtual screening and evaluation would significantly widen the design space for highly efficient phosphorescent dopants with unbiased measures to evaluate performance of the materials from first principles.
Organic light-emitting diode (OLED) devices are under widespread investigation to displace or complement inorganic optoelectronic devices for solid-state lighting and active displays. The materials in these devices are selected or designed according to their intrinsic and extrinsic electronic properties with concern for efficient charge injection and transport, and desired stability and light emission characteristics. The chemical design space for OLED materials is enormous and there is need for the development of computational approaches to help identify the most promising solutions for experimental development. In this work we will present examples of simulation approaches available to efficiently screen libraries of potential OLED materials; including first-principles prediction of key intrinsic properties, and classical simulation of amorphous morphology and stability. Also, an alternative to exhaustive computational screening is introduced based on a biomimetic evolutionary framework; evolving the molecular structure in the calculated OLED property design space.
Simulations of the optical intensity within Nano Imprint Lithography (NIL) mask features have been made for patterned quartz masks having ultrathin film coatings with different indices of refraction. Fractionally fluorine terminated surfaces, previously proposed for improving the yield of NIL processes, are briefly reviewed. Optical intensity solutions within the feature were obtained using Panoramictech Maxwell solver software for variances in the optical constants of the coating films, aspect ratio, feature size, and wavelength.. The coated masks have conformal surface, higher index of refraction under-layer coating and a fractional terminated fluorine hydrocarbon (FHC) monomolecular layer. The values of optical constants for the FHC layers are unknown, so a range of ad-hoc values were simulated. Optical constants for quartz mask and Al2O3, TiO2 and Si under-layer films are taken from the literature. Wavelengths were varied from 193nm to 365nm. The question of photo-dissociation of the FHC layer for higher energy photons is addressed from first principles, with the result that the F-terminated layers are stable at higher wavelengths. Preliminary simulations for features filled with resist over various substrates are dependent on the antireflection character of the underlying film system. The optical intensity is generally increased within the simulated mask feature when coated with a higher index/FHC films relative to the uncoated reference quartz mask for ~5nm physical feature sizes.
Organic light-emitting diodes (OLEDs) are under widespread investigation to displace or complement inorganic optoelectronic devices for solid-state lighting and active displays. The materials comprising the active layers in OLED devices are selected or designed to provide the required intrinsic and extrinsic electronic properties needed for efficient charge injection and transport, and desired stability and emissive properties. The chemical design space for OLED materials is enormous and there is need for the development of computational approaches to help identify the most promising chemical solutions for experimental development. In this work we present a multi-scale simulation approach to efficiently screen libraries of potential OLED molecular materials. The workflow to assess potential OLED materials is: 1) evaluation based on first-principles prediction of key intrinsic properties (EHOMO, ELUMO, λe/h, Etriplet), 2) classical simulation of thin film morphology (RDF, ρ), and 3) first-principles evaluation of electron coupling for donor-acceptor pairs (Hab) from the simulated condensed phase morphology.
A structure and method for coating Nano Imprint Lithography (NIL) masks is described. The approach uses conformal ALD layering methods and sequential monomolecular depositions. The processes describe chemically bonded, high density, smooth coatings having fractional fluorine terminations. Various molecular precursor mixtures or various reactive surface site chemical functionalization schemes allow the attainment of controlled percentages of fractional F-terminations. The percentage of fluorine terminations is adjustable and controllable from 0% to 100%. Chemistries are described that result in coating layers of the order of ~1nm. These fractional F-terminated coatings may be useful for the reduction and minimization of defects in advanced imprint lithography processes.
Computational structure enumeration, analysis using an automated simulation workflow and filtering of large chemical structure libraries to identify lead systems, has become a central paradigm in drug discovery research. Transferring this paradigm to challenges in materials science is now possible due to advances in the speed of computational resources and the efficiency and stability of chemical simulation packages. State-of-the-art software tools that have been developed for drug discovery can be applied to efficiently explore the chemical design space to identify solutions for problems such as organic light-emitting diode material components. In this work, virtual screening for OLED materials based on intrinsic quantum mechanical properties is illustrated. Also, a new approach to more reliably identify candidate systems is introduced that is based on the chemical reaction energetics of defect pathways for OLED materials.
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