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Cheng et al. J. Mater. Inf. 2025, 5, 53 https://dx.doi.org/10.20517/jmi.2025.61 Page 3 of 17

improvement (EI) functions to efficiently capture the complex relationship between the ultimate tensile
[31]
strength (UTS) and electrical conductivity, enabling the ML-based inverse design of Cu-Ni-Si alloys .
Padhy et al. developed a multi-objective Bayesian optimization (MOBO)-based inverse design strategy to
[32]
combine the magnetic, electrical, and mechanical properties to accurately design Fe-Co-Ni alloys .
However, the application of ML to Mg alloys remains relatively limited, as research in this area is still in its
early stages and mainly focuses on predicting their structures and properties rather than on alloy design.

In recent years, high-performance Mg alloys, particularly those based on Gd and Y, have attracted
considerable attention. The resulting body of reliable data provides a solid foundation for applying ML to
their design. Developing effective methods for designing such alloys can help overcome the efficiency
bottleneck caused by extensive experimental testing and characterization. To achieve simultaneous
optimization of multiple mechanical properties, advanced multi-objective optimization strategies are
therefore essential. Ghorbani et al. implemented an active learning framework that combined Gaussian
process regression with Bayesian optimization to efficiently optimize the multi-objective performance of
strength and ductility in Mg alloys, providing valuable technical insights for the field . In contrast, the
[33]
present study establishes a novel ML-based framework that enables rapid inverse screening and multi-
objective optimization of Mg alloy compositions and processing parameters. Unlike Bayesian optimization
methods, which rely on iterative sampling and are suited for data-scarce scenarios, the proposed RF-NSGA-
II (RF combined with NSGA-II) framework adopts a global search strategy that fully exploits existing
database resources. Moreover, it yields the complete set of Pareto-optimal solutions in a single run,
providing diverse candidate alloys for efficient materials design.

The present work develops an inverse design framework for Mg-Gd-based alloys by employing ML and a
multi-objective co-optimization strategy. Based on the collected data for extruded Mg-Gd and Mg-Y alloys
and the identification of key compositional and processing parameters, the optimized forward model was
developed by evaluating the performance of multiple ML algorithms, including RF, eXtreme gradient
boosting, support vector regression (SVR), and multilayer neural networks. It properly describes the
relationship between chemical composition and mechanical properties, including tensile yield strength
(TYS), UTS, and elongation (EL). The NSGA-II is then implemented to simultaneously optimize essential
mechanical properties using the established forward model, which is validated through experimental
measurements. Furthermore, the inverse design framework is adopted to efficiently develop advanced Mg-
Gd-based alloys tailored to the desired mechanical properties.

MATERIALS AND METHODS
Data preparation
The dataset employed in present work was compiled from a wide range of published experimental studies
on extruded Mg-Gd and Mg-Y-based alloys. The key parameters considered include alloy composition
(types and contents of alloying elements), processing parameters [solid solution temperature (ST), solid
solution time (St), extrusion temperature (ET), and extrusion ratio (ER)], and mechanical properties (TYS,
UTS, and EL). The raw data was cleaned to remove missing and duplicate entries, and feature selection was
performed, resulting in approximately 400 available data points. The variation ranges (minimum and
maximum limits) of each feature variable are summarized in Table 1. It is worth noting that the lower
bounds for the alloying elements were set to zero to preserve a broader design space and avoid artificially
excluding potentially high-performance compositions, particularly in the absence of well-defined critical
concentration thresholds.