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Page 2 of 17 Cheng et al. J. Mater. Inf. 2025, 5, 53 https://dx.doi.org/10.20517/jmi.2025.61

INTRODUCTION
Magnesium (Mg) alloys are promising lightweight structural materials for engineering applications in
aerospace, automotive, and transportation due to their low density and high specific strength and
stiffness . However, the relatively low strength and ductility of Mg alloys severely limit their widespread
[1-3]
[4,5]
applications . The incorporation of rare earths (RE) into the Mg matrix has been demonstrated to
significantly enhance the mechanical properties. Particularly, Mg alloys containing Gadolinium (Gd) and
Yttrium (Y) show exceptional potential for high-performance applications owing to their excellent solid
solution strengthening and age hardening effects [6-10] . The synergistic interaction of Zn, Zr, and Mn with RE
elements (Gd/Y) can further enhance the alloy performance by facilitating the formation of a long-period
stacking ordered phase (LPSO) [11,12] , secondary phase particles , and the refined microstructure .
[13]
[14]
Additionally, thermomechanical processes such as extrusion can further improve the mechanical properties
of Mg alloys by refining the microstructure through dynamic recrystallization and precipitation phase
modulation [9,15,16] .

Generally, the development of high-performance Mg alloys depends on the synergistic interaction of
alloying elements, processing techniques, and other pertinent variables. As the array of alloying elements
expands and processing technologies evolve, the interplays between them become very complex and are not
straightforward to understand. This dynamic interaction complicates the correlation between mechanical
properties of Mg alloys and their compositional and processing parameters. Currently, the design of Mg
alloys predominantly relies on an experimental trial-and-error approach, which is laborious and inefficient.
Traditional thermodynamic and kinetic models, based on well-established physical principles, provide
reliable predictions when the underlying mechanisms are clear. Nevertheless, they often face challenges in
modeling complex, multi-factor coupled systems and obtaining the necessary critical parameters. Given this
context, there is an urgent necessity to devise a novel method capable of rapidly capturing the relationship
between the mechanical properties of Mg alloys and their composition-processing parameters. To this end,
the present study proposes a novel machine learning (ML)-based inverse design framework. It integrates the
random forest (RF) and non-dominated sorting genetic algorithm II (NSGA-II), which enables rapid multi-
objective optimization and provides a transparent route for the inverse design of advanced Mg alloys.

ML, a rapidly emerging data-driven technology, effectively uncovers implicit relationships within extensive
datasets. It is distinguished by its low computational cost and short development cycle . ML has been
[17]
widely applied across various domains of computer science, including computer vision [18,19] and natural
[20]
language processing . In the field of materials, there has been growing interest in ML for compound
discovery, structural property prediction, new materials discovery, and pharmaceutical molecular design. In
a recent study, Rahnama et al. employed a decision tree regression algorithm to predict the hydrogen weight
percentage of hydrogen storage metal hydrides . Liu et al. accurately predicted the structure of 891 ABO3
[21]
compounds with 94.6% accuracy using a gradient boosting decision tree (GBDT) . In addition, Ghorbani
[22]
et al. systematically compared several ML algorithms, including Lasso, kernel ridge regression, RF, and
neural networks, for predicting tensile properties, providing valuable insights into alloy performance
[23]
modeling . These applications highlight the potential of ML in the field of materials science, particularly in
predicting alloy properties and guiding design optimization. By effectively describing the relationship
between material composition and properties, ML facilitates the understanding of the underlying laws
governing material-related properties and thus avoids the unnecessary expenditure associated with
redundant research . Meanwhile, significant progress has been made in ML-based alloy inverse design
[24]
research, including the development of global optimization methods , active learning [26,27] , and generative
[25]
networks [28,29] . It should be noted that Bayesian optimization has recently evolved beyond traditional global
extremum search to address goal-oriented inverse design tasks . Qin et al. combined two expectation
[30]