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. Dr. Jorge Muñoz and his group study the quantum mechanical origins of the phase stability of materials by developing computational, data science, and machine learning methods and tools to extend the reach of first-principles atomistic simulations of materials. Changes induced by pressure, temperature, chemical ordering, etc. in the electronic structure and magnetism of elemental metals and metallic alloys and their effect on the phonons and phonon entropy are of particular interest. The group also develops machine learning pipelines and data science solutions for applied problems in physics.