Computer aided design of stable and efficient OLEDs

Abstract

Organic light emitting diodes (OLEDs) offer a unique alternative to traditional display technologies. Tailored device architecture can offer properties such as flexibility and transparency, presenting unparalleled application possibilities. Commercial advancement of OLEDs is highly anticipated, and continued research is vital for improving device efficiency and lifetime. The performance of an OLED relies on an intricate balance between stability, efficiency, operational driving voltage, and color coordinates, with the aim of optimizing these parameters by employing an appropriate material design. Multiscale simulation techniques can aid with the rational design of these materials, in order to overcome existing shortcomings. For example, extensive research has focused on the emissive layer and the obstacles surrounding blue OLEDs, in particular, the trade-off between stability and efficiency, while preserving blue emission. More generally, due to the vast number of contending organic materials and with experimental pre-screening being notoriously time-consuming, a complementary in silico approach can be considerably beneficial. The ultimate goal of simulations is the prediction of device properties from chemical composition, prior to synthesis. However, various challenges must be overcome to bring this to a realization, some of which are discussed in this Perspective. Computer aided design is becoming an essential component for future OLED developments, and with the field shifting toward machine learning based approaches, in silico pre-screening is the future of material design.

Artificial intelligence: A powerful paradigm for scientific research

“…Recently, AI techniques have made significant advances in rational design and accelerated discovery of various materials, such as piezoelectric materials with large electrostrains, 67 organic-inorganic perovskites for photovoltaics, 68 molecular emitters for efficient light-emitting diodes, 69 inorganic solid materials for thermoelectrics, 70 and organic electronic materials for renewable-energy applications. 66 , 71 The power of data-driven computing and algorithmic optimization can promote comprehensive applications of simulation and ML (i.e., high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning, etc.…”

Virtual Screening for Organic Solar Cells and Light Emitting Diodes

“…An interesting example of an application‐driven molecular design is the optimization of compounds in an organic light emitting diode (OLED). [ 20 , 21 ] A modern state‐of‐the‐art OLED in general possesses a multilayer device architecture, which is composed of two electrodes, electron/hole injection layers, [ 22 ] electron/hole transport layers and emission layers, as shown in Figure 2 . Injection of electrons and holes in such a device leads to both singlet (25%) and triplet (75%) excited states.…”

28‐1: Invited Paper: Bottom‐Up OLED Development by Virtual Design: Systematic Elimination of Performance Bottlenecks Using a Microscopic Simulation Approach

“…Despite recent advances (18) such predictive models to fully connect fundamental chemistry and device design still struggle due to the high level of complexity: a large ensemble of effects and processes needs to be considered, ranging from charge transport in pristine layers via injection processes towards complex excitonic processes in mixed co-emission systems (7,8,(19)(20)(21)(22). Due to the complex interplay of these processes, device performance is the consequence of a fine balance of time scales of all processes involved, and the final outcome of predictive simulations is highly sensitive to the accuracy of the model for each individual process (23).…”