Computational physics and data science with a focus on fundamental excitations in solids

Core research

Our group studies the quantum mechanical origins of the phase stability of materials. We are particularly interested in how changes induced by pressure, temperature, chemical ordering, etc. in the electronic structure and magnetism of elemental metals and metallic alloys affect their phonons and phonon entropy. Phase stability is one of the most fundamental phenomena in the universe, and prediction of structure and properties of materials using computers is nowadays one of the most important problems in applied science.

Computational physics and data science

We develop computational methods to extract information, typically from dynamics simulations data, about how crystal lattices interact with excitations and about the thermodynamics and transport properties of the systems. We also use these data to train machine learning models, currently based on either equivariant neural networks or mathematical graphs with marginalized graph kernels, and use the models to extend the reach of the dynamics simulations. Finally, we use statistical analysis and data science techniques to make robust conclusions on our data.

Other research topics

Our expertise in simulation of physical phenomena, machine learning, and data science, allows us to establish productive collaborations with domain experts in other fields. Members of our group are often co-mentored by faculty members and scientists who are experts in other areas. In the past we have studied atmospheric turbulence and propagation of light in turbulent media. We currently have active collaborations on neutron stars and nuclear astrophysics, analysis of data from particle colliders, geoinformatics, and more.

Funding