Research Methods

Our group is primarily computational/theoretical, with a small but growing experimental presence in collaboration with Prof. Jay Whitacre, focused on integrating machine learning approaches with automated experimentation. We have a strong commitment to using and contributing to open-source software development efforts. We’re always learning more, but the current methods/tools most commonly used in the group are summarized here.

• Electronic structure calculations: Density Functional Theory using GPAW and the Atomic Simulation Environment ( ASE)
• Scientific Machine Learning in both Julia and Python
• Phase-field modeling: MOOSE
• Crystal structure search/prediction: CALYPSO
• Bayesian optimization with Dragonfly
• Nearly all of the above rely on high-performance computing resources on the shared College of Engineering Cluster mananged by our group, Arjuna, as well as at national leadership computing facilities such as NERSC and XSEDE