Non-technical Abstract: This project aims to revolutionize the discovery of new solid-state materials that can precisely control the mobility of ions and electrons, an essential step toward building the next generation of energy storage systems, neuromorphic computers, and smart sensors. By leveraging advanced artificial intelligence (AI), machine learning (ML), and automated synthesis tools, the team will develop a transformative approach to design solid-state ion conductors using multi-element doping, enabling materials tailored for next-generation energy and electronic systems. A central goal is to establish a new data-driven approach to achieve an optimal balance of ion and electron conductivities for targeted applications while ensuring material stability during operation, a task difficult to achieve using traditional trial-and-error techniques. The project will also provide hands-on research and training opportunities in AI-driven materials discovery, fostering collaboration among U.S. and Canadian universities, national laboratories, and industry partners. Technical Abstract: This research will develop and apply a closed-loop, data-driven framework to design and optimize multi-element co-doping strategies in alkali-ion conductors. By integrating AI/ML-accelerated property prediction, high-throughput computational modeling, autonomous synthesis, and in-situ characterization, this project will systematically investigate how co-doping influences ionic transport, elec