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

International Journal of Minerals,Metallurgy and Materials 2025年第2期32卷 439-449页

作者： Jia Zhao Taixi Feng Guimin Lu National Engineering Research Center for Integrated Utilization of Salt Lake Resource East China University of Science and TechnologyShanghai 200237China Joint International Laboratory for Potassium and Lithium Strategic Resources East China University of Science and TechnologyShanghai 200237China

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.

关键词： magnesium-lanthanum liquid alloys local structure macroscopic properties thermodynamic behavior deep potential mo-lecular dynamic simulation

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Lattice approaches to packed column simulations

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Particuology 2008年第6期6卷 404-411页

作者： Richard Caulkin Mike Fairweather Richard A. Williams Institute of Particle Science and Engineering School of ProcessEnvironmental and Materials EngineeringUniversity of Leeds

This work presents a review of the findings into the ability of a digitally based particle packing algorithm, called DigiPac, to predict bed structure in a variety of packed columns, for a range of generic pellet shapes frequently used in the chemical and process engineering industries. Resulting macroscopic properties are compared with experimental data derived from both invasive and non-destructive measurement techniques. Additionally, fluid velocity distributions, through samples of the resulting bed structures, are analysed using lattice Boltzmann method （LBM） simulations and are compared against experimental data from the literature.

关键词： Packing algorithm Bed structure Packed columns macroscopic properties Lattice Boltzmann simulations

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