OLED Device ML
Machine learning solution to investigate relationships between the architecture and performance of OLED devices for accelerated screening
Create machine learning models to enable high-throughput design and optimization of OLED devices
The OLED Device ML solution enables scientists to predict performance metrics that quantify the operational output, efficiency, and stability of multicomponent layered organic light-emitting diodes (OLEDs). These predictions are based upon simple and direct descriptions of device operation and architecture, such as the arrangement and chemical composition of layers. This offers a scalable solution for OLED developers seeking to perform targeted evaluations of device capability across novel design spaces.
Key Capabilities
Train chemistry-informed ML models to predict performance properties for OLED devices with varying layer arrangements and chemical compositions
Rapidly predict the performance of novel device structures to establish interpretable relationships between functionality and layer architectures and chemistry
Use pre-trained ML models to predict six different device performance metrics including external quantum efficiency, current efficiency, power efficiency, electroluminescence maximum peak position, electroluminescence bandwidth, and color of the emitted light
Benefit from an intuitive graphical interface that allows easy design and exploration of novel chemistry and device architectures, facilitated by the visualization of energy level diagrams with out-of-the-box QM descriptors and ML models
Broad applications across materials science research areas
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Support & Training
Access expert support, educational materials, and training resources designed for both novice and experienced users.