Computational materials science uses mathematical models of atoms and atomic interactions to predict material properties. The main focus of our group is on structural materials, which directly impacts energy efficiency by improving processing capabilities of lighter materials (leading to weight reduction in automotive, aerospace, and naval transportation applications) or improving high temperature properties for materials in energy production (leading to increased operational temperatures for turbines in aerospace and reactors).

We study chemical effects on mechanical properties of materials, such as plasticity and phase transformations, as well as fundamental properties of defects in materials, such as dislocations and interfaces. Dislocations are key to understanding plastic deformation in metals. We use a range of techniques, such as density-functional theory, classical potentials, and tight-binding, combined with new computational techniques that we're developing including lattice Green function-based flexible boundary conditions, density-functional theory energy density, and stochastic/statistical methods; finally, we're able to make predictions about larger-scale material properties with mathematical modeling. We've applied these methods to a variety of materials, including

• Diffusion of interstitials and substitutional solutes, includes the effects of correlated motion
• Mechanical properties of magnesium alloys
• Titanium mechanical properties, phase transformations, and kinetics of oxygen
• Hydrogen in palladium at dislocations
• Copper islands on silver surfaces to understand heteroepitaxial island diffusion
• Gold nanoparticles supported on titania surfaces for catalysis
• Mechanical properties of aluminum alloys
• Equation of state for molten nickel-based superalloys
• Solid-solution softening in molybdenum
• Thermal properties of high-pressure phases of ice