Collaborative Research: DMREF:NSF-NSERC: Data-Driven Multi-Element Doping for Optimally Controlled Ion-Electron Conduction

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, electronic structure, and lattice stability across bulk phases, grain boundaries, and interfaces. A fast, iterative inner loop will enable the screening of thousands of dopant combinations, while a slower outer loop will focus on extracting mechanistic insights and ensuring scalability, feeding knowledge back into the predictive models. Target systems include sodium- and lithium-ion based oxides and halides, where varying the balance of ionic and electronic conduction is critical for applications ranging from batteries to neuromorphic computing. The project will generate foundational design rules for tuning transport properties through co-doping, creating new pathways for energy-efficient materials innovation. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.