Key Takeaways & Executive Findings
- •• Machine learning-driven deep potential molecular dynamics (DPMD) accurately predicts the local structure and thermophysical properties of Mg–La liquid alloys, overcoming the accuracy-efficiency dilemma of traditional simulation methods. • The effect of increasing Mg content on the first coordination shell mirrors the effect of increasing temperature, providing a unified understanding of structural evolution in Mg–La melts. • A medium-range ordered structure, indicated by a pre-peak in the structure factor, is prominent in the 80at% Mg alloy and vanishes at elevated temperatures, revealing temperature-sensitive ordering. • The study establishes a reliable database of density, self-diffusion coefficients, and shear viscosity for Mg–La alloys, and evaluates mixing enthalpy and elemental activity at 1200 K, offering new guidance for alloy design and processing.
Abstract
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.
1. Introduction
The addition of lanthanum (La) to magnesium (Mg) alloys could enhance their performance, including increasing the Mg alloy’s corrosion resistance [1], regulating their grain growth, and enhancing their mechanical properties [2–3], etc., thus boosting the product level of Mg alloys and improving their competitiveness in applications such as 3C products [4], the automotive industry [5], and the aerospace industry [6].
The properties of the melt metal determine the process of filling the casting and the physical–chemical processes during crystallization, and are therefore fundamental to the quality of castings. The structural, physical, and thermodynamic behavior of metals in the liquid state are intimately linked to the structure of their solid state. Thus, without a comprehensive understanding of the melt, it is impossible to achieve high-quality castings with optimum performance at a minimum cost. On the other hand, thermophysical descriptions of the Mg–La alloy are essential for revealing its chemical behavior and physical properties [7]. Therefore, an in-depth understanding of the local structure and thermophysical behavior of melt alloys could facilitate the identification of the optimum solidification process to produce a well-performing material, promoting the development of related industries.
Although metal melts have been analyzed by experimental techniques such as high-temperature X-ray diffraction [8–9] and synchrotron radiation experiments [10–11], the experimental investigation of melts encounters plenty of challenges, such as the complexity of high-temperature tests and their high sensitivity to external perturbations. Fortunately, computational simulation sheds light on this dilemma.
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Jia Zhao, Taixi Feng, Guimin Lu (2025). Understanding the local structure and thermophysical behavior of Mg–La liquid alloys via machine learning potential. Int. Journal of Minerals, Metallurgy and Materials (矿物冶金与材料学报). https://doi.org/10.1007/s12613-024-2928-2
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Frequently Asked Questions
What is the main objective of this study?
The study aims to understand the local structure and thermophysical behavior of Mg–La liquid alloys using machine learning-driven deep potential molecular dynamics (DPMD) simulation, providing a reliable and efficient method to predict properties and guide alloy development.
How was the deep potential model validated?
The deep potential model was validated by comparing root-mean-square errors (RMSEs) for energy and force, and by comparing structural information from simulations with reference data, confirming its reliability.
What key structural findings were reported?
The study found that increasing Mg content affects the first coordination shell similarly to increasing temperature. A pre-peak in the structure factor indicated medium-range order, especially pronounced in the 80at% Mg system, which disappears at high temperatures.
What thermophysical properties were predicted?
The study predicted density, self-diffusion coefficient, and shear viscosity for Mg–La liquid alloys as functions of composition and temperature, and also evaluated mixing enthalpy and elemental activity at 1200 K.
What is the significance of this research for industry?
The findings provide a comprehensive database and insights into the melt behavior of Mg–La alloys, which can help optimize casting processes and improve the quality and performance of Mg-based products in automotive, aerospace, and electronics industries.
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