Abstract
The shear viscosity of matter and efficient simulating methods in a wide range of temperatures and densities are desirable. In this study, we present the deep-learning many-body potential (the deep potential) method to reduce the computational cost of simulations for the viscosity of liquid aluminum at high temperature and high pressure with accurate results. Viscosities for densities of 2.35 g/cm3, 2.7 g/cm3, 3.5 g/cm3, and 4.27 g/cm3 and temperatures from melting points to about 50 000 K are calculated. The results agree well with the experiment data at a pressure near 1 bar and are consistent with the simulation of first-principles at high pressure and high temperature. We reveal the behavior of the shear viscosity of liquid Al at a range where the current experimental results do not exist. Based on the available experimental data and newly generated simulation data, we propose a modified Enskog–Dymond theory, which can analytically calculate the viscosity of Al at this range. This research is helpful for numerous potential applications.
Highlights
The transport properties of matter in a wide range of temperatures and densities have been widely investigated due to the applications in a variety of cutting-edge sciences.1–4 In particular, the shear viscosity plays an important role in the inertial confinement fusion (ICF) capsules,5–9 modeling astrophysical objects,10 simulating the magnetic generator of the outer core,11 understanding microjetting during shock-loading,12 and wave damping in dense plasmas.13 The measurements for viscosity have been conducted at a pressure near 1 bar, and it is very difficult to implement at high temperature and high pressure.14,15 The viscosity of metals obtained from the shock-loading experiment usually is the effective strength in solids, and the experimental data in fluids are sparse
We develop the Deep Potential (DP) method to calculate the viscosity of liquid metal and Al is estimated for a prototype due to the applications of the hydrodynamic modeling setting microjetting model under different shock-loading12 and treating the stability of initial interface
We compare the results of DP based molecular dynamics (DPMD) with the results of QMD16 and experiments.40,41. It reveals that DPMD agree with experiments at ambient pressure
Summary
The transport properties of matter in a wide range of temperatures and densities have been widely investigated due to the applications in a variety of cutting-edge sciences. In particular, the shear viscosity plays an important role in the inertial confinement fusion (ICF) capsules, modeling astrophysical objects, simulating the magnetic generator of the outer core, understanding microjetting during shock-loading, and wave damping in dense plasmas. The measurements for viscosity have been conducted at a pressure near 1 bar, and it is very difficult to implement at high temperature and high pressure. The viscosity of metals obtained from the shock-loading experiment usually is the effective strength in solids, and the experimental data in fluids are sparse. Alfè and Gillan employed the quantum molecular dynamics (QMD) based on first-principles to calculate the viscosity using the Green–Kubo relations, which is a pioneering study that applied first-principles to calculate viscosity. Their time step and simulation length for aluminum (Al) were 3 fs and 80 ps, respectively, which obtained only two state points of Al. Jakse and Pasturel employed QMD within local-density (LDA) and generalized gradient approximations (GGAs) to obtain data of four state points at normal pressure and temperature for Al, and the former (LDA) was found to be better compared with experimental data. It is significant to develop a method that can efficiently and accurately predict the viscosity
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