Transferable interatomic potential for aluminum from ambient conditions to warm dense matter

Abstract

We present a study on the transport and material properties of aluminum spanning from ambient to warm dense matter conditions using a machine-learned interatomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulate phenomena in warm dense matter, but these potentials have often been calibrated for a narrow range of temperatures and pressures. In contrast, we train a single ML-IAP over a wide range of temperatures, using density functional theory molecular dynamics (DFT-MD) data. Our approach overcomes the computational limitations of DFT-MD simulations, enabling us to study the transport and material properties of matter at higher temperatures and longer time scales. We demonstrate the ML-IAP transferability across a wide range of temperatures using molecular dynamics by examining the ionic part of thermal conductivity, shear viscosity, self-diffusion coefficient, sound velocity, and structure factor of aluminum up to about 60000 K, where we find good agreement with previous theoretical data.