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.
@article{arxiv.2304.09703,
title = {Transferable Interatomic Potentials for Aluminum from Ambient Conditions to Warm Dense Matter},
author = {Sandeep Kumar and Hossein Tahmasbi and Kushal Ramakrishna and Mani Lokamani and Svetoslav Nikolov and Julien Tranchida and Mitchell A. Wood and Attila Cangi},
journal= {arXiv preprint arXiv:2304.09703},
year = {2023}
}