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We present a study on the transport and materials properties of aluminumspanning from ambient to warm dense matter conditions using a machine-learnedinteratomic potential (ML-IAP). Prior research has utilized ML-IAPs to simulatephenomena in warm dense matter, but these potentials have often been calibratedfor a narrow range of temperature and pressures. In contrast, we train a singleML-IAP over a wide range of temperatures, using density functional theorymolecular dynamics (DFT-MD) data. Our approach overcomes computationallimitations of DFT-MD simulations, enabling us to study transport and materialsproperties of matter at higher temperatures and longer time scales. Wedemonstrate the ML-IAP transferability across a wide range of temperaturesusing molecular-dynamics (MD) by examining the thermal conductivity, diffusioncoefficient, viscosity, sound velocity, and ion-ion structure factor ofaluminum up to about 60,000 K, where we find good agreement with previoustheoretical data.

Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter