Efficient calculations of equation-of-state data in the warm-dense matter regime

Equation-of-state (EoS) data — relating the pressure and internal energy to material density and temperature — is a key quantity in the warm dense matter regime, for example as input to hydrodynamics codes used to guide inertial confinement fusion experiments. The first-principles methods, density-functional theory and path-integral Monte–Carlo, are considered state-of-the-art approaches to calculate EoS data. However, both methods are computationally expensive, which motivates the development of low-cost approaches such as average-atom models. In the first part of this talk, we benchmark EoS results from an average-atom model against the extensive first-principles dataset from Militzer et al. (Phys. Rev. E 103, 013203). In the second part, we develop a neural-network surrogate model as a numerically feasible alternative to calculating EoS data. We train two neural networks to interpolate this dataset, with one being trained using average-atom outputs and the other without. We also compare the accuracy of the machine-learned and average-atom models using out-of-distribution data from other sources.