Lithium Isotope Separation

This project aims to develop artificial intelligence (AI) and machine learning (ML)-guided approaches to identify porous materials suitable for lithium isotope separation, a key challenge for future nuclear energy technologies. By integrating data-driven modeling with experimental and computational studies, this works seeks to understand how nanoscale confinement influences isotope-selective lithium ion transport. Rather than designing entirely new materials, this work focuses on screening and analyzing existing porous materials, such as metal–organic frameworks (MOFs), to identify structural features and conditions that enable selective transport of ⁶Li and ⁷Li. Inspired by ion-channel-like transport in confined environments, the project will investigate how pore structure, coordination environments, and ion dynamics affect isotope selectivity. The insights gained will guide the identification of optimal materials and operating conditions for lithium isotope separation. Beyond lithium systems, this framework may also be extended to other challenging isotope separations, including hydrogen and deuterium, relevant to advanced energy technologies.