Accurate thermodynamic tables for solids using Machine Learning Interaction Potentials and Covariance of Atomic Positions

Finite-temperature thermodynamic properties such as entropy and free energies of solids are notoriously difficult to compute accurately despite their importance in many standard calculations. Experimental data only exists at easy to operate conditions for specific phases, limiting the ability to predict phase and thermodymic behavior at extreme conditions such as high pressure. The difficulty in calculating thermodynamic properties comes from the very high cost of more accurate methods such as Density Functional Theory as well as the long simulation times needed for methods such as thermodynic integration. To overcome these issues, we use Machine Learning Interaction Potentials (MLIPs) to allow fast but still accurate calculations and calculate entropy from the covariance of atomic positions. The combination of these two methods allows the accurate parallelized computing of thermodynamic properties for arbitrary phases at arbitrary pressure and temperature. We perform these calculations for alkali metal systems and find excellent agreement with experimental measurements.