Understanding the local structure and thermophysical behavior of Mg–La liquid alloys via machine learning potential

The local structure and thermophysical behavior of Mg–La liquid alloys were in-depth understood using deep potential molecular dynamic (DPMD) simulation driven via machine learning to promote the development of Mg–La alloys. The robustness of the trained deep potential (DP) model was thoroughly evaluated through several aspects, including root-mean-square errors (RMSEs), energy and force data, and structural information comparison results; the results indicate the carefully trained DP model is reliable. The component and temperature dependence of the local structure in the Mg–La liquid alloy was analyzed. The effect of Mg content in the system on the first coordination shell of the atomic pairs is the same as that of temperature. The pre-peak demonstrated in the structure factor indicates the presence of a medium-range ordered structure in the Mg–La liquid alloy, which is particularly pronounced in the 80at% Mg system and disappears at elevated temperatures. The density, self-diffusion coefficient, and shear viscosity for the Mg–La liquid alloy were predicted via DPMD simulation, the evolution patterns with Mg content and temperature were subsequently discussed, and a database was established accordingly. Finally, the mixing enthalpy and elemental activity of the Mg–La liquid alloy at 1200 K were reliably evaluated, which provides new guidance for related studies.