We have a variety of research interests relating to computational materials design.
Systems and Applications
We primarily study metal surfaces and nanoparticles for chemical applications, such as catalysis. As such, we focus on the interaction between metals and organic materials. We also study excitations in metals, with possible applications in catalysis, solar fuels, and solar cells. Finally, we are also very interested in developing methods that are useful in studying materials in general.
Our main focus is developing accurate, general, and efficient models for studying and designing materials. While density functional theory (DFT) is accurate enough to be useful in materials design, it is slow. Therefore, we take data from DFT calculations and construct more efficient models based on both machine learning and our understanding of physics and chemistry. We also apply a variety of thermodynamic and kinetic models to apply the DFT calculations to longer length or timescales or to realistic conditions.
EFFICIENT DESIGN OF CATALYSTS
We have developed a general understanding of how reaction intermediates bind to metal surfaces. This provides insight into the design of catalysts, and also allows more efficient screening. We are currently working on improving and applying our models.
MACHINE LEARNING AND DATA SCIENCE FOR MATERIALS DESIGN
We have leveraged data scientific techniques for understanding various aspects of catalysis, and are working to develop more general methodologies. In particular, we are working to use both data science (to ensure accuracy) and physical insight (to ensure generality) to develop models that are applicable to a variety of problems.
EXCITED STATE DYNAMICS
We have shown that electronic excitations occur during thermal reactions on nanoparticles, and these excitations can affect surface chemistry. We are currently studying how excitations in metal nanoparticles transfer to molecules and semiconductors.