Organic semiconductors are critical components for advancing next-generation flexible and lightweight electronic devices, including organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), and organic field-effect transistors (OFETs). Despite significant progress, the development of high-performing organic semiconductors remains a time-intensive and resource-intensive process due to the vast chemical design space.

Generative AI algorithms, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), offer a novel approach to accelerate the discovery of organic semiconductors by efficiently navigating this chemical space. These algorithms can design new molecular structures with targeted electronic, optical, and structural properties, significantly reducing the trial-and-error nature of traditional experimental methods.

This project focuses on leveraging generative AI techniques to discover novel organic semiconductors tailored for specific applications in optoelectronics, enabling rapid innovation in materials science.

Objectives

1. Develop Generative AI Models: Build and train generative models to design novel organic semiconductor candidates optimized for target properties
2. Establish a Structure-Property Dataset: Curate a high-quality dataset of known organic semiconductors with annotated properties to train and validate the generative models
3. Explore Molecular Design Rules: Use explainable AI techniques to identify key molecular substructures and structure-property relationships driving performance
4. Accelerate Discovery: Generate and prioritize novel candidates for synthesis and experimental validation

Expected Outcomes

1. A robust generative AI pipeline for designing novel organic semiconductors with tailored properties
2. A high-quality dataset and property-prediction models for organic semiconductor materials
3. Discovery of new molecular structures optimized for specific applications in optoelectronics
4. Insights into structure-property relationships for organic semiconductors, guiding future material development