Research Philosophy:

Our group frequently identifies problems that have impact, and works backward to identify or develop the set of tools required to solve the problem. Therefore we employ and develop computational approaches in a variety of spaces, from DFT, time-domain DFT, molecular dynamics, empirical interatomic potentials, and machine learning.

Our predictions that have been subsequently experimentally confirmed:

Our group has made many theoretical predictions that have been subsequently observed in the laboratory. We tend to publish our predictions well before the experiments are performed, demonstrating the truly predictive nature of our work. Among the key values of our work is guiding lab researchers to materials and effects that they would not otherwise discover on their own.

Methodological Advances:

When necessary, our group has advanced the technology for computing the properties of materials. One fashion we do this is to clearly describe in our publications how we employ existing tools to compute something that has not been previously computed. In other instances, we have developed new algorithms and software in spaces including molecular dynamics, machine learning applications to materials, time-domain DFT with plane waves, and others. Selected examples include: