Computational Thermodynamics and Machine Learning

Core research

Our group studies the origin of energetic and entropic contributions to the free energy of conventional and nuclear matter and how they affect their materials properties, thermodynamics, and phase stability. To do so, we use a combination of physics simulations, mathematical modeling, and machine learning algorithms. We develop ourselves many of the methodologies that we use in our research.

Current research interests

Phase stability of iron at the pressure and temperature conditions found in the inner core of the earth, dynamical (phonon) stability of crystal structures beyond the Born elastic criteria, machine learning with mathematical graphs, parameterization and characterization of the nuclear matter equation of state.

Funding