Transferable interatomic potential for aluminum from ambient conditions to warm dense matter
Sandeep Kumar, Hossein Tahmasbi, Kushal Ramakrishna, Mani Lokamani, Svetoslav Nikolov, Julien Tranchida, Mitchell Wood, Attila Cangi
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.