Atomistic simulations on liquid Mg-Sr alloys assisted with deep learning potential

J Zhao and TX Feng and GM Lu, JOURNAL OF MATERIALS SCIENCE, 59, 13575-13590 (2024).

DOI: 10.1007/s10853-024-09937-2

It has been demonstrated that Sr is a beneficial additive element to Mg alloys and could effectively upgrade the properties of Mg alloys. However, the local structure and thermophysical properties of Mg-Sr, which are important properties of the alloy, have rarely been reported. This work applied atomistic simulations, assisted by deep learning potential (DPL), conducted on the Mg-Sr liquid alloy to reveal its local structure and thermophysical properties. The reliability of the trained DPL model was thoroughly validated by the root-mean-square errors, energy and force comparison results, and local structure reproducibility. The radial distribution function and structural factor were adopted to assess the short-range and intermediate-range ordering of the Mg-Sr liquid alloy, whose component and temperature dependence were analyzed. Mg-Sr alloy is more compact in the Mg-rich system; all the ordering is temperature-negatively dependent. Densities, self- diffusion coefficients, and shear viscosities of Mg-Sr liquid alloys containing different Mg concentrations, covering the temperature range of 1100-1400 K, were predicted by deep learning molecular dynamics simulation, and the corresponding databases were established. The enthalpies of mixing and elemental activities of Mg-Sr liquid alloys at 1100 K were reliably evaluated. The mixing enthalpy is less than zero, and the dependence of the Mg and Sr activities on the Mg concentration presents a downward concave shape that deviates from Raoul's law, forming a negative deviation. Overall, this present study provides theoretical foundation and directional guidance for the development of Mg-Sr alloys.

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