Machine-Learning Potential Driven Simulations of Carbon in the Expanded Regime

Experimental validation of equation of state (EOS) models for high-energy-density applications typically involves measuring the shock Hugoniot, which helps constrain the high-pressure, high-density EOS. However, EOS measurements at below ambient density in the so-called expanded regime are less common. Recent experimental advances allow the isentrope to be inferred from measurements of the density profile of an adiabatically expanding proton-heated plasma, offering the potential to assess various theoretical EOS models over a wide range of below ambient density conditions.

On the theoretical side, determining the expanded regime EOS poses a considerable challenge for ab initio methods like density functional theory based molecular dynamics (DFT-MD) due to the big simulation cells and large number of orbitals required to simulate these low density, high temperature conditions. We thus take a more computationally friendly approach by leveraging the power of MD driven by quantum accurate machine learning potentials. We directly simulate the adiabatic expansion of proton-heated diamond at conditions relevant to experiments using multi-million atom cells. From these simulations, we both extract the isentrope and elucidate the microscopic chemistry at play, such as the formation of graphene- or carbyne-like rings and chains. These insights gained from our MD simulations can serve to guide and interpret experimental design and observations.