Dynamics of Solvated Ion Transport in Confined Pore Environments

With support from the Chemical Structure and Dynamics (CSD) program in the Division of Chemistry, Professor Francesco Paesani of the University of California, San Diego and Professor Mircea Dincă of Princeton University are investigating the molecular mechanisms that govern ion transport in confined environments and using the resulting insights to design metal-organic frameworks (MOFs) as quasi-solidstate electrolytes (QSSEs). MOFs are porous, chemically tunable materials with the potential to enable safer and more efficient energy storage systems. However, the mechanisms controlling ion migration in MOFs remain poorly understood. This project integrates computer simulations, advanced spectroscopic measurements, and artificial intelligence (AI) to study how pore geometry, cation identity, solvent environment, and chemical functionalization affect ion solvation and mobility under confinement. Professors Paesani and Dincă will work with their teams to characterize ion–solvent–framework interactions and free-energy landscapes and to establish predictive structure–function relationships. Their discoveries could guide the design of MOFs optimized for ion conduction and contribute to a broader framework for materials with tunable transport properties. Educational activities will engage undergraduate and graduate students in interdisciplinary training across computational chemistry, experimental materials chemistry, and AI for molecular and materials discovery. This research integrates experimental methods, such as solid-state nuclear magnetic resonance (NMR), vibrational spectroscopy, and electrochemical impedance spectroscopy, with data-driven many-body molecular dynamics (DD-MB MD) simulations that employ highly accurate potential energy functions to model confined ion–solvent systems at the molecular level. Together, these tools will be used to investigate how MOF structure, pore chemistry, solvent composition, and applied electric fields influence solvation structures, transport mechanisms, and free-energy landscapes. The data generated from these studies will be used to train a physics- and chemistry-informed AI platform designed to identify and optimize MOFs for QSSE applications through high-throughput screening of large structural databases. By integrating simulation, experiment, and AI, this project will establish a scalable and transferable framework for understanding and designing materials that control molecular transport in confined environments. The resulting design principles will advance not only the development of next-generation QSSEs, but also porous materials for applications in catalysis, sensing, nanofluidics, and environmental separations. 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.