Research
Overview
Research in the Fong Group integrates simulation and theory to advance fundamental understanding and guide the rational design of sustainable technologies for clean water and energy. Our work relies heavily on statistical mechanics and molecular dynamics simulations, including the development of machine learning-based interatomic potentials that enable accurate, large-scale molecular simulations. These techniques are supplemented with elements of continuum-scale modeling and quantum chemistry.
Optimizing transport in lithium-ion battery electrolytes
The performance of current Li-ion batteries is limited in part by the properties of the electrolyte: sluggish motion of Li-ions through the electrolyte restricts the rate at which the battery can be charged or discharged and lowers the energy efficiency of the system. Design of improved electrolyte formulations is hindered by our inability to relate the macroscopic transport behavior probed experimentally to the microscopic motion of the electrolyte constituents. To address this challenge, we develop theory based on nonequilibrium statistical mechanics to map between macroscopic electrolyte behavior and molecular-scale ion motion. This provides a powerful lens for intuitively interpreting battery electrolyte performance and informing the design of improved electrolyte formulations.
Nanoconfined electrolyte solutions
Nanoporous membranes have the potential to efficiently enable many key separations processes at the water-energy nexus, from water desalination to critical mineral recovery. However, the physical chemistry of an electrolyte solution confined within a nanopore differs drastically from that of a bulk electrolyte, rendering many of our standard theories for electrolyte thermodynamics and transport largely invalid. We use molecular simulations to investigate the structural and dynamic properties of complex nanoconfined electrolytes, ultimately aiming to inform the design of nanoporous environments that enable rapid and selective ion transport.
Multiscale modeling of electrochemical systems
A key challenge in bridging theory and experiment of electrochemical systems is linking molecular- and continuum-level behavior. We aim to develop multiscale modeling frameworks that link atomistic phenomena, such as ion solvation, mobility, and surface adsorption, with continuum-level descriptions of transport and thermodynamics. We focus on systems where coupled gradients in concentration, electric potential, and/or pressure drive complex ion dynamics at electrolytes and electrochemical interfaces. We work to describe these effects with first-principles-level accuracy by incorporating machine learning potentials trained on quantum chemical data.
