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Cheng et al. J. Mater. Inf. 2025, 5, 53 https://dx.doi.org/10.20517/jmi.2025.61 Page 11 of 17

Table 4. The predicted composition and processing parameters from the RF-NSGA inverse design model
Composition (wt.%) Processing parameters Predicted Target
Alloy
Gd Y Zn Mn ST (°C) St (h) ET (°C) ER UTS/MPa EL/% UTS/MPa EL/%
Alloy 1 11.0 5.8 1.1 0.1 515 16 455 10 400.3 5.1 400 5
Alloy 2 2.4 0.0 1.5 0.0 490 20 400 22 250.1 19.1 250 20
RF: Random forest; NSGA: non-dominated sorting genetic algorithm; ST: solid solution temperature; St: solid solution time; ET: extrusion
temperature; ER: extrusion ratio; UTS: ultimate tensile strength; EL: elongation.

Figure 5. The Pareto front of validation alloys generated by the RF-NSGA inverse design model: (A) Alloy 1 with the target UTS =
400 MPa and EL = 5%; and (B) Alloy 2 with the target UTS = 250 MPa and EL = 20%. RF: Random forest; NSGA: non-dominated
sorting genetic algorithm; UTS: ultimate tensile strength; EL: elongation.

Figure 6. Room-temperature tensile stress-strain curves of (A) Alloy 1 and (B) Alloy 2.

existing dataset boundaries represents a critical aspect of practical application. In this context, the
established RF-NSGA inverse design model was employed to design Mg-Gd-based alloys at the edge of the
dataset to achieve improved mechanical properties. Given the potential of different combinations of
chemical compositions and processing parameters at the data edge to reach target properties, the inverse
design process was configured with an expanded decision space, as detailed in Table 5. It should be noted
that, although the decision space was broadened to some extent, the compositional and processing
parameters remained within physically and technologically reasonable limits.