His research aims to integrate atomistic modeling, neutron scattering, and advanced data analysis to understand and predict fundamental processes in structural and functional materials. To implement this integrated approach, he has been developing software to enable virtual neutron scattering experiments using common atomistic models as input, and to expand the scope of the computer models using machine learning algorithms. His recent scientific interests include host-guest interactions in chemical systems such as catalysts and metal-organic frameworks, solid-state ionic conductors for batteries and fuel cells, and amorphous/disordered metals and semiconductors.