Lithium metal batteries have a theoretical specific energy rivaling gasoline and could provide practical specific energies equivalent to gasoline. However, they face significant challenges before they are commercially viable. Our research in this area focuses on understanding interfacial stability with different material systems and operating conditions.

Decreasing the levels of carbon dioxide in the atmosphere is critical to fighting climate change. The development of carbon capture and conversion technologies that can remove carbon dioxide and convert it to value added products is a promising research area. Our research in this area focuses developing machine learning informed models of carbon capture and conversion.

Porous hierarchical materials are found in many energy and sustainability related systems from fuel cells and batteries to water filtration and biomass upgrading. Our research aims to understand the interplay of structure and chemistry in these systems and how integrated computational and experimental research can be used to more efficiently design new material systems.

Interfaces within complex material systems are where critical chemical-physical processes occur and where performance degradation usually starts. The meso-scale imbedded nature of interfaces makes them challenging to experimentally resolve. In our research we develop computational models to understand the transport, heat transfer, and reactions occurring in interfacial regions for energy and suitability technologies.

Integration of machine learning and data science methods with physical modeling, such as computational fluid dynamics, can help to accelerate materials development, better understand parameter space, and enhance analysis of experimental and computational data. Our research focuses on applying machine learning and data science methods for applications of uncertainty quantification, reduced order modeling, design of experiments, and more.