Our group uses quantum chemistry-based multiscale modeling to predict and study the atomic scale of materials and chemical reactions. With the combination of electronic structure and atomistic models as well as kinetic modeling, we can investigate fundamental reaction steps at different time- and length-scales that would otherwise be difficult or impossible to investigate with experiment. 

Our modeling studies are entirely carried out in silico (on a computer) using quantum chemistry data. This makes our modeling predictions largely free from artificial biases that are present when using experimental inputs that may have unexpected uncertainties. Whether making computational predictions alone or in direct collaboration with experimentalists, our group provides deep perspective on the atomic-scale nature of chemical bonding in molecules and materials.  

Our 'ground-up' multiscale modeling approach uses appropriate levels of quantum chemistry (QC) theory (typically on up to ca. 200 atoms) to model reaction energies, barrier heights, pKas, and standard redox potentials. Using data obtained from QC theory, we can also develop of analytic reactive forcefields or machine learned methods, which are capable of modeling reaction dynamics on systems on the order of 100,000 atoms. The atomistic data in turn can be used to generate rate constant libraries for kinetic Monte Carlo (kMC) simulations to model larger time-scale and length-scale phenomena such a nanoparticle/material growth and ripening.