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

J Zhao and TX Feng and GM Lu, INTERNATIONAL JOURNAL OF MINERALS METALLURGY AND MATERIALS, 32, 439-449 (2025).

DOI: 10.1007/s12613-024-2928-2

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.

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