Identification of host-guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study

Deep Learning Prediction of Triplet-Triplet Annihilation Parameters in Blue Fluorescent Organic Light-Emitting Diodes

The triplet-triplet annihilation (TTA) ratio and the rate coefficient (kTT) of TTA are key factors in estimating the contribution of triplet excitons to radiative singlet excitons in fluorescent TTA organic light-emitting diodes. In this study, deep learning models are implemented to predict key factors from transient electroluminescence (trEL) data using new numerical equations. A new TTA model is developed that considers both polaron and exciton dynamics, enabling the distinction between prompt and delayed singlet decays with a fundamental understanding of the mechanism. In addition, deep learning models for predicting the kinetic coefficients and TTA ratio are established. After comprehensive optimization inspired by photophysics, determination coefficient values of 0.992 and 0.999 are achieved in the prediction of kTT and TTA ratio, respectively, indicating a nearly perfect prediction. The contribution of each kinetic parameter of polaron and exciton dynamics to the trEL curve is discussed using various deep-learning models.

A materials informatics driven fine-tuning of triazine-based electron-transport layer for organic light-emitting devices

Materials informatics in the development of organic light-emitting diode (OLED) related materials have been performed and exhibited the effectiveness for finding promising compounds with a desired property. However, the molecular structure optimization of the promising compounds through the conventional approach, namely the fine-tuning of molecules, still involves a significant amount of trial and error. This is because it is challenging to endow a single molecule with all the properties required for practical applications. The present work focused on fine-tuning triazine-based electron-transport materials using machine learning (ML) techniques. The prediction models based on localized datasets containing only triazine derivatives showed high prediction accuracy. The descriptors from density functional theory calculations enhanced the prediction of the glass transition temperature. The proposed multistep virtual screening approach extracted the promising triazine derivatives with the coexistence of higher electron mobility and glass transition temperature. Nine selected triazine compounds from 3,670,000 of the initial search space were synthesized and used as the electron transport layer for practical OLED devices. Their observed properties matched the predicted properties, and they enhanced the current efficiency and lifetime of the device. This paper provides a successful model for the ML assisted fine-tuning that effectively accelerates the development of practical materials.

Perspectives on development of optoelectronic materials in artificial intelligence age

Optoelectronic devices, such as light-emitting diodes, have been demonstrated as one of the most demanded forthcoming display and lighting technologies because of their low cost, low power consumption, high brightness, and high contrast. The improvement of device performance relies on advances in precisely designing novelty functional materials, including light-emitting materials, hosts, hole/electron transport materials, and yet which is a time-consuming, laborious and resource-intensive task. Recently, machine learning (ML) has shown great prospects to accelerate material discovery and property enhancement. This review will summarize the workflow of ML in optoelectronic materials discovery, including data collection, feature engineering, model selection, model evaluation and model application. We highlight multiple recent applications of machine-learned potentials in various optoelectronic functional materials, ranging from semiconductor quantum dots (QDs) or perovskite QDs, organic molecules to carbon-based nanomaterials. We furthermore discuss the current challenges to fully realize the potential of ML-assisted materials design for optoelectronics applications. It is anticipated that this review will provide critical insights to inspire new exciting discoveries on ML-guided of high-performance optoelectronic devices with a combined effort from different disciplines.

Charge transporting and thermally activated delayed fluorescence materials for OLED applications

The design and synthesis of effective charge transporting (CT) and thermally activated delayed fluorescence (TADF) materials are in high demand to obtain high-performing OLED devices. Recently, the significant development in the field of OLEDs has led to the creation of numerous charge transporting and TADF materials with diverse structures. To further improve the device performance, a better understanding of the structural characteristics and structure-property relationships of these materials is essential. Moreover, to enhance the efficiency of OLEDs, all the electrogenerated excitons should be constrained in EMLs. The TADF mechanism can theoretically register 100% IQE through a potent up-conversion method from non-radiative triplet excitons to radiative singlet excitons. In this review, the structural importance, classification, physical properties, and electroluminescence data of some recent charge transporting and TADF materials are summarized and discussed. Moreover, their molecular structural dependence on functional groups and linkers is classified, which can enhance their charge transporting or emitting ability. To offer a potential roadmap for the further development of charge transporting and TADF materials, it is hoped that this study will encourage researchers to acknowledge their important role in OLEDs.

Design of Thermally Activated Delayed Fluorescence Materials with High Intersystem Crossing Efficiencies by Machine Learning-Assisted Virtual Screening

Efficient intersystem crossing (ISC) and reverse ISC (RISC) processes are of vital significance for thermally activated delayed fluorescence (TADF) materials to achieve 100% internal quantum efficiency. However, it is challenging to rapidly predict the ISC/RISC rates of large amounts of TADF materials and screen promising candidates because of their flexible molecular design. Here, we perform virtual screening of 564 candidates constructed from 20 unique building blocks linking in D-A, D-π-A, and D-A-D (D') configurations using the established machine learning models of GBRT and RF-GBRT-KNN with the Pearson's correlation coefficients (r) of 0.89 and 0.82, respectively. Novel descriptors of ΔE_LL, Polar, and ΔE_TT for predicting ISC/RISC rates were proposed, and nine TADF molecules with the predicted ISC and RISC rates of >7 × 10⁷ and 2 × 10⁵ s^-1, respectively, were revealed. We provide an efficient approach to predicting ISC and RISC rates of TADF molecules on a large scale, elucidating important building blocks and architectures to design high-performance optoelectronic materials for experimental explorations.

Understanding the charge dynamics in organic light-emitting diodes using convolutional neural network

Knowledge about the charge dynamics in organic light-emitting diodes (OLEDs) is a critical clue to optimize device architecture for enhancing the power efficiency and driving voltage characteristics in addition to the external quantum efficiency. In this work, we demonstrated that the charge behavior according to the operation voltage of OLEDs could be understood by introducing the convolutional neural network (CNN) of the machine learning framework without additional analysis of the unipolar charge devices. The CNN model trained using a two-dimensional (2D) modulus fingerprint simultaneously predicted the mobilities of the charge transport and emitting layers, realizing a deep understanding of the complicated data that humans cannot interpret. The machine learning model successfully describes the electrical properties of the organic layers in the actual devices configurated by different electron-transporting materials and the composition of cohosts in the emitting layer. For the first time, it was revealed that 2D fingerprints extracted using frequency- and voltage-dependent modulus spectra were effective data to represent comprehensive charge dynamics of OLEDs. The interpretation and perspective of the machine learning approach in this work were also discussed.

Predicting the Redox Potentials of Phenazine Derivatives Using DFT-Assisted Machine Learning

This study investigates four machine-learning (ML) models to predict the redox potentials of phenazine derivatives in dimethoxyethane using density functional theory (DFT). A small data set of 151 phenazine derivatives having only one type of functional group per molecule (20 unique groups) was used for the training. Prediction accuracy was improved by a combined strategy of feature selection and hyperparameter optimization, using the external validation set. Models were evaluated on the external test set containing new functional groups and diverse molecular structures. High prediction accuracies of R ² > 0.74 were obtained on the external test set. Despite being trained on the molecules with a single type of functional group, models were able to predict the redox potentials of derivatives containing multiple and different types of functional groups with good accuracies (R ² > 0.7). This type of performance for predicting redox potential from such a small and simple data set of phenazine derivatives has never been reported before. Redox flow batteries (RFBs) are emerging as promising candidates for energy storage systems. However, new green and efficient materials are required for their widespread usage. We believe that the hybrid DFT-ML approach demonstrated in this report would help in accelerating the virtual screening of phenazine derivatives, thus saving computational and experimental costs. Using this approach, we have identified promising phenazine derivatives for green energy storage systems such as RFBs.