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Cheng et al. J. Mater. Inf. 2025, 5, 53 Journal of
DOI: 10.20517/jmi.2025.61
Materials Informatics

Research Article Open Access

RF-NSGA-II framework for inverse design of high-
performance Mg-Gd-based magnesium alloys

1,2
1,2
1,2
Yunchuan Cheng 1,2,# , Lei Wang 1,2,# , Zhihua Dong 1,2,* , Zengyong Zheng , Zhihong Xia , Shengwen Bai ,
1,2
Jiangfeng Song , Bin Jiang 1,2
1
National Engineering Research Center for Magnesium Alloys, College of Materials Science and Engineering, Chongqing
University, Chongqing 400044, China.
2
National Key Laboratory of Advanced Casting Technologies, College of Materials Science and Engineering, Chongqing
University, Chongqing 400044, China.
#
The authors contributed equally to this work.
* Correspondence to: Prof. Zhihua Dong, National Engineering Research Center for Magnesium Alloys, College of Materials Science
and Engineering, Chongqing University, Chongqing 400044, China. E-mail: dzhihua@cqu.edu.cn
How to cite this article: Cheng, Y.; Wang, L.; Dong, Z.; Zheng, Z.; Xia, Z.; Bai, S.; Song, J.; Jiang, B. RF-NSGA-II framework for
inverse design of high-performance Mg-Gd-based magnesium alloys. J. Mater. Inf. 2025, 5, 53. https://dx.doi.org/10.20517/jmi.
2025.61

Received: 4 Jul 2025 First Decision: 21 Aug 2025 Revised: 6 Sep 2025 Accepted: 23 Sep 2025 Published: 14 Nov 2025

Academic Editors: Xiang-Dong Ding, Sheng Sun, Xingjun Liu Copy Editor: Pei-Yun Wang Production Editor: Pei-Yun Wang

Abstract
An inverse design framework, RF-NSGA-II, is developed using machine learning (ML) methods and a multi-
objective co-optimization strategy. It enables intelligent design of chemical compositions and processing
parameters for thermo-mechanical treatments based on desired mechanical properties of Mg alloys. Using a
database of extruded Mg-Gd and Mg-Y-based alloys, RF-NSGA-II integrates an optimized forward model with a
non-dominated sorting genetic algorithm II (NSGA-II). The forward model is constructed by evaluating the
performance of different ML algorithms, with the random forest (RF) algorithm experimentally validated to
accurately describe the relationship between chemical composition and mechanical properties. RF-NSGA-II
simultaneously optimizes multiple mechanical properties, and validation through experimental measurements
demonstrates its effectiveness. Using target mechanical properties as inputs, chemical compositions and
processing parameters for solid-solution treatment and extrusion are efficiently determined for a high-strength
Mg-11.5Gd-6.0Y-1.0Zn-0.2Mn (wt.%) alloy and a high-ductility Mg-2.5Gd-1.0Zn (wt.%) alloy, achieving tensile
strength/elongation values of 417 MPa/3.2% and 223 MPa/34%, respectively. These results provide a transparent
and effective route for the inverse design of advanced Mg alloys based on desired mechanical properties.

Keywords: Magnesium alloys, machine learning, inverse design, mechanical properties

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