Research Areas

We apply molecular simulations and machine learning to design advanced electrolytes with improved ion transport, stability, and safety, accelerating the development of next-generation batteries for clean energy storage.

With the rapid growth of electric vehicles and renewable energy storage, the demand for lithium has surged, creating both supply chain and sustainability challenges. Our research investigates computational methods to enhance lithium commodity recovery and separation processes from spent batteries and natural resources. By combining molecular simulations and machine learning, we aim to design more efficient solvents, membranes, and separation strategies that enable sustainable lithium recycling and extraction, reducing environmental impact while supporting the future of clean energy.

We design and optimize Drude polarizable force fields for ions and nanomaterials, capturing polarization effects essential for accuracy. These customized models support breakthroughs in battery electrolytes, molecular separations, and bridging molecular insights with real-world energy applications.

We also explore molecular simulations and AI to design advanced membranes and porous materials for selective gas separation, capture, and storage.