Extending ab initio simulations for the ion-ion structure factor of warm dense aluminum to the hydrodynamic limit using neural network potentials

Abstract

We calculate the intermediate scattering function of warm dense aluminum by using density functional theory molecular dynamics simulations. From this data set, we derive the static and dynamic ion-ion structure factors. By applying a generalized collective modes model, we can fit the excitation spectra of the ion system and thereby extract the dispersion for the ion acoustic modes, as well as the decay coefficients for the diffusive and collective modes. The results are discussed and compared with experimental data if available. We show that computational limitations prevent sufficient access to the hydrodynamic limit and demonstrate that this can be circumvented using high-dimensional neural network potentials. We extract the ionic thermal conductivity of aluminum in the hydrodynamic limit and compare to values computed using a Green-Kubo relation. We highlight the importance of partitioning the heat capacity into electronic and ionic contributions and only using the ionic contribution to compute the thermal conductivity of the ions in the hydrodynamic limit.