The world’s first big data and intelligent design platform for magnesium materials, “MagNova”, jointly developed by Mingyue Lake Laboratory, Chongqing University, and the National Engineering Research Center for Magnesium Alloys, was officially launched.

Mg alloys have garnered significant attention in advanced engineering applications due to their exceptional combination of specific strength and lightweight properties. The α-Mg solid solution, which constitutes the core component of Mg alloys, directly governs the alloy’s thermodynamic behavior, kinetic response, and overall performance. This paper systematically reviews the effects of the α-Mg matrix phase on mechanical properties (e.g., when the average grain size of pure Mg is refined from 59.7 µm to 1.57 µm, the alloy’s yield strength increases by 122 MPa), corrosion resistance (e.g., adding 0.5% Gd and 0.5% Sc to pure Mg reduces the alloy’s weight loss rate from 208.71 mm/year to 0.29 mm/year), damping properties, electromagnetic shielding properties, and flame retardancy, and reveals the microscopic interaction mechanisms between the matrix phase, solute atoms, crystal defects, and second phases. Furthermore, this paper critically analyzes the key role of the matrix phase in emerging fields such as bio-Mg alloys (e.g., controlled degradation rate design), Mg-air batteries (e.g., anode efficiency optimization), and Mg-based hydrogen storage materials (e.g., enhancement of hydrogen absorption/desorption kinetics). Finally, this paper explores the significant potential of data-driven methods in the design and development of next-generation high-performance Mg alloys.

Research on magnesium (Mg) alloys still remains a prominent and expanding field in recent years. The Web of Science Core Collection database documented 4898 published articles on the topic, highlighting a sustained and growing interest. Statistical analysis of the literature reveals a consistent focus on microstructures, mechanical and corrosion properties. Significant progress has also been made in the manufacture of large-scale Mg alloy components. Meanwhile, steady advancements have been achieved in functional magnesium materials, magnesium-based hydrogen storage, and magnesium-ion batteries, with magnesium-based Energy Storage Mater. moving closer to commercial applications. Notably, the year 2024 marks a breakthrough in artificial intelligence, and the integration of big data and artificial intelligence is expected to significantly accelerate the research and development of magnesium alloy materials. Furthermore, the decline in primary magnesium prices in 2024 has triggered a new wave of research and large-scale commercial applications. Concurrently, there is growing interest in their use in emerging industries such as unmanned aerial vehicles and robotics. With continuous improvements and diversification in performance, the applications of magnesium alloys have expanded significantly in 2024, encompassing satellite components, integrated automotive structures, magnesium alloy formwork, and biomedical materials. This paper provides a comprehensive review of the current state of development and key research challenges in the field of Mg alloys as of 2024, and also outlines potential future directions for research and application.

The performance of Mg alloys is significantly influenced by the concentrations and solid solution behavior of the alloying elements. In this work, the solid solution behavior of 20 alloying elements in 190 ternary Mg alloy systems at 500 ℃ are systematically investigated. The solid solution behavior of a set of two different alloying elements in Mg alloy systems are suggested to be classified into three categories: inclusivity, exclusivity and proportionality. Inclusivity classification indicates that the two alloying elements are inclusive in α-Mg, increasing the joint solubility of both elements. Exclusivity classification suggests that the two alloying elements have a low joint solid solubility in α-Mg, since they prefer to form stable second phases. For the proportionality classification, the solubility curve of the ternary Mg alloy systems is a straight line connecting the solubility points of the two sub-binary systems. The proposed classification theory was validated by key experiments and the calculation of formation energies. The interaction effects between alloying elements and the preference of formation of second phases are the main factors determining the solid solution behavior classifications. Based on the observed solid solution features of multi-component Mg alloys, principles for alloy design of different types of high-performance Mg alloys were proposed in this work.

The solution behavior of a second element in the primary phase (α(Mg)) is important in the design of high-performance alloys. In this work, three sets of features have been collected: a) interaction features of solutes and Mg obtained from first-principles calculation, b) intrinsic physical properties of the pure elements and c) structural features. Based on the maximum solid solubility values, the solution behavior of elements in α(Mg) are classified into four types, e.g., miscible, soluble, sparingly-soluble and slightly-soluble. The machine learning approach, including random forest and decision tree algorithm methods, is performed and it has been found that four features, e.g., formation energy, electronegativity, non-bonded atomic radius, and work function, can together determine the classification of the solution behavior of an element in α(Mg). The mathematical correlations, as well as the physical relationships among the selected features have been analyzed. This model can also be applied to other systems following minor modifications of the defined features, if required.