First-principles calculations of the viscosity in multicomponent metallic melts: Al-Cu-Ni as a test case.

• Deep machine learning potential is applied for simulating viscosity of Al-Cu–Ni melts. • Results match experimental viscosity dependencies on temperature and concentration. • Simulations reproduce the minimum of the viscosity at the eutectic composition. • Deep machine learning potentials predict viscosity of multicomponent metallic melts. Calculating viscosity in multicompoinent metallic melts is a challenging task for both classical and ab initio molecular dynamics simulations methods. The former may not to provide enough accuracy and the latter is too resources demanding. Machine learning potentials provide optimal balance between accuracy and computational efficiency and so seem very promising to solve this problem. Here we address simulating kinematic viscosity in ternary Al-Cu–Ni melts with using deep neural network potentials (DP) as implemented in the DeePMD-kit. We calculate both concentration and temperature dependencies of kinematic viscosity in Al-Cu–Ni and conclude that the developed potential allows one to simulate viscosity with high accuracy; the deviation from experimental data does not exceed 12% and is close to the uncertainty interval of experimental data. More importantly, our simulations reproduce minimum on concentration dependency of the viscosity at the eutectic composition. Thus, we conclude that DP-based MD simulations is highly promising way to calculate viscosity in multicomponent metallic melts. [ABSTRACT FROM AUTHOR]