Calculating viscosity in multicompoinent metallic melts is a challenging task for both classical and \textit{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 9\% 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 point. Thus, we conclude that DP-based MD simulations is highly promising way to calculate viscosity in multicomponent metallic melts.
Machine learning potential as a guide for eutectic in ultra-refractory multicomponent ceramics
Moscow Institute of Physics and Technology (National Research University)（莫斯科物理科学与技术大学）；Institute for High Pressure Physics, Russian Academy of Sciences（俄罗斯科学院高压物理研究所）；Joint Institute for Nuclear Research（苏联联合核研究所）
A general machine learning model of aluminosilicate melt viscosity and its application to the surface properties of dry lava planets
Université Paris Cité（巴黎城市大学）；Institut de physique du globe de Paris（巴黎地球物理研究所）；CNRS-UMR 7154（法国国家科学研究中心-7154联合研究单位）；Institut Universitaire de France（法国大学研究院）；York University（约克大学）；ETH Zürich（苏黎世联邦理工学院）；Institute of Geochemistry and Petrology（地球化学与岩石学研究所）
Molecular Dynamics simulations of Al-Ti metallic alloy melts using a transferable machine-learning potential
School of Materials and Chemical Technology, Institute of Science Tokyo（东京科学大学物质理工学院）；Institute for Frontier Materials on Earth and in Space, German Aerospace Center（德国航空航天中心地球与太空前沿材料研究所）；Institut Laue-Langevin (ILL)（朗之万研究所）；Institute for Theoretical Physics, Heinrich-Heine-Universität Düsseldorf（杜塞尔多夫大学理论物理研究所）
