Copyedit to max 500 words: The needs of future e-societies rely heavily on advances in data processing, component miniaturization, and secure communications. To achieve this, new materials and their processing techniques must be developed with improved reliability, performance, and productivity. Recent advances in machine learning and automation have enabled the development of autonomous AI-based systems, which have the potential to revolutionize optoelectronic materials research. This project seeks to design and develop an autonomous AI-based materials research platform capable of successfully identifying, synthesizing, and optimizing materials using a closed-loop approach. Such a system would rely on the integration of lab automation systems, robotics, and machine learning algorithms into a single platform, able to generate new knowledge on materials and their processing for applications in optoelectronic components, devices, and systems. The platform will focus on four specific applications: novel metal oxide ultra-wide band-gap semiconducting materials for high-frequency and power electronic applications; novel electrode materials for zinc ion batteries as safe and sustainable power sources for IoT devices; novel metal oxide compositions as high-k dielectric materials for next generation power and flexible electronics; and novel organic optoelectronic materials for power and wearable electronics.