Research Projects

Warm dense matter (WDM) is a state of matter with densities ranging from solid density to a few orders higher than solid density and temperatures ranging from a few eV to a few keV. It is relevant for various applications, including the interior of gas giants and exoplanets, inertial confinement fusion, and ablation of metals. Current experimental campaigns in photon sources rely on numerical simulations, such as density functional theory-molecular dynamics (DFT-MD) simulations. However, two challenges impede progress: (1) DFT-MD becomes computationally infeasible with increasing temperature and (2) finite-size effects render many computational observables inaccurate. Recently, molecular dynamics simulations using machine learning-based interatomic potentials (ML-IAPs) could overcome these limitations. In the national postdoctoral fellowship, the author will study the transport and material properties of WDM using machine-learned interatomic potentials (ML-IAPs) over the temperature and pressure range. This will help accurately predict the melting line and shock dynamics in the temperature and pressure range where these calculations are not feasible with DFT-MD simulations.