  We present a study on the transport and materials 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 temperature 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 computational
limitations of DFT-MD simulations, enabling us to study transport and materials
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 (MD) by examining the thermal conductivity, diffusion
coefficient, viscosity, sound velocity, and ion-ion structure factor of
aluminum up to about 60,000 K, where we find good agreement with previous
theoretical data.
