My group's research is centered on computational materials science, and specifically first-principles quantum mechanical simulation tools. These computational tools have advanced to the point now where materials may be "synthesized virtually", with their properties predicted on a computer before ever being synthesized in a laboratory.  These tools also open the field of "materials informatics" where we can use machine learning tools to explore materials datasets and discover new materials.  In this work, we are working towards a goal of being able to suggest new materials in the same way that Netflix and Amazon can recommend movies or books.

While the types of materials problems amenable to these tools is extremely wide, we are currently interested in a variety of materials problems with a focus on materials for alternative energies and sustainability (hydrogen, batteries, light-weight metals, fuel cells, thermoelectrics). Current topics of interest include the discovery of novel hydrogen storage materials, phase transformations in metallic and ceramic alloys, microstructural evolution during aging, and the theoretical prediction of new materials.

Another key research interest involves methodologies for linking atomistic and microstructural length scales. Though first-principles methods are powerful, they are also computationally quite demanding. Current state-of-the-art resources limits the system sizes that one can study to around a few hundred atoms. We have worked on methods that couple first-principles with Monte Carlo methods (capable of simulating millions of atoms), phase-field microstructural models, and CALPHAD calculation of phase diagram tools. These types of hybrid methods are yielding truly predictive models of microstructural evolution and mechanical properties in novel materials.