Non-Technical Description: The development of solution-processable semiconductor materials has the potential to revolutionize emerging technologies in electronics and quantum engineering technologies by enabling scalable, cost-effective manufacturing methods. Among these materials, metal halide perovskite nanocrystals exhibit exceptional properties suited for advanced technological applications. However, their widespread adoption faces significant limitations due to presence of heavy metals, such as lead. This project aims to accelerate the discovery of high-performance, lead-free perovskite nanocrystals through the integration of high-throughput experimentation, artificial intelligence (AI), and advanced data-sharing strategies across multiple institutions. By establishing networked "self-driving laboratories" (SDLs) capable of autonomously exploring extensive materials synthesis parameter spaces, this research is expected to drastically shorten discovery timelines from years to weeks or months. The project's broader impacts include the development of new educational programs designed to train a skilled workforce proficient in AI-driven and autonomous scientific research methodologies, thereby promoting broad participation in innovative STEM careers. Technical Description: This research addresses the critical challenge of discovering lead-free metal halide perovskite nanocrystals by establishing distributed SDLs that integrate automated flow chemistry systems, colloida