Many advanced electronic devices – such as OLEDs, batteries, solar cells, and transistors – rely on complex multilayer architectures composed of multiple materials. Optimizing device performance, stability, and efficiency requires precise control over layer composition and arrangement, yet experimental exploration of new designs is costly and time-intensive. Although physics-based simulations offer insight into individual materials, they are often impractical for full device architectures due to computational expense and methodological limitations.

Schrödinger has developed a machine learning (ML) framework that enables users to predict key performance metrics of multilayered electronic devices from simple, intuitive descriptions of their architecture and operating conditions. This approach integrates automated ML workflows with physics-based simulations in the Schrödinger Materials Science suite, leveraging physics-based simulation outputs to improve model accuracy and predictive power. This advancement provides a scalable solution for rapidly exploring novel device design spaces – enabling targeted evaluations such as modifying layer composition, adding or removing layers, and adjusting layer dimensions or morphology. Users can efficiently predict device performance and uncover interpretable relationships between functionality, layer architecture, and materials chemistry. While this webinar focuses on single-unit and tandem OLEDs, the approach is readily adaptable to a wide range of electronic devices.

Key highlights:

• A machine learning framework for modeling electronic device performance, allowing users to define architectural features to explore novel device configurations
• Model accuracy demonstrated with a dataset of over 2,000 OLED architectures for multiple key performance metrics
• Pre-trained ML models for six device performance metrics available out-of-the box, including external quantum efficiency, current efficiency, power efficiency, electroluminescence maximum peak position, bandwidth, and emission color
• Intuitive graphical interface for designing, training, and exploring new chemistries and device architectures
• Demonstration of the framework’s extensibility to a broad range of electronic devices

Who should attend:

• Device developers
• R&D leaders
• Innovation managers
• Digitization managers
• Synthetic chemists
• Computational materials scientists