Core researchMy group studies the phase stability of materials. We are interested in how changes induced by pressure, temperature, chemical ordering, etc. in the electronic structure and magnetism of metals and 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 scienceWe 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 Gaussian process regression on mathematical graphs with marginalized graph kernels, and use the models to extend the reach of the dynamics simulations.Other research topicsOur expertise in physics simulations, machine learning, computational methods, and thermodynamics, allows us to establish productive collaborations with domain experts in other fields. We have longstanding collaborations with nuclear physicists to study nuclear matter using the same or modified versions of the tools we develop for ordinary matter.