Accelerating age hardening behavior of Mg alloy using a data-driven screening strategy guided by machine learning

In this work, we introduce a self-guided design framework that combines target property and feature importance evaluation to optimize the aging behavior of Mg alloys. The design efficiency was built by a precise and reliable two-step feature engineering training strategy (2SFE) guided by machine lea...

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Bibliographic Details
Published in:Next materials Vol. 10; p. 101425
Main Authors: Moses, Atwakyire, Huang, Qiao, Chen, Ding
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2026
Elsevier
Subjects:
Age-hardening
Machine learning
Mechanical property
Mg alloys
Variable feature reduction
Variable feature reduction
Mechanical property
Mg alloys
Age-hardening
Machine learning
ISSN:2949-8228, 2949-8228
Online Access:Get full text
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Summary:In this work, we introduce a self-guided design framework that combines target property and feature importance evaluation to optimize the aging behavior of Mg alloys. The design efficiency was built by a precise and reliable two-step feature engineering training strategy (2SFE) guided by machine learning models capable of assessing a range of Mg alloy properties across high-dimensional datasets. A multi-objective hybrid algorithm combines efficient global optimization (EGO) and a gradient-based feature reduction is established within this framework. EGO incorporates an innovative multi-objective gradient designed to speed up the search process along the Pareto front scheme, while 2SFE reduces features to improve the design space. A novel two-step feature engineering selection strategy (2SFE) was introduced, reducing computational input features to six (6) while enhancing prediction accuracy. Compared to the traditional approach, the scaled super learner model improved accuracy from 0.86 to 0.97. This methodology was experimentally validated, resulting in the creation of a novel Mg-6Gd-Zr-6Y-Nd, Mg-9Zn-5Gd-Zr-8Y-3Nd, Mg-8Zn-Zr-3Y-Nd, and Mg-9Zn-Zr-2Y (wt%) alloy, processed under defined specific conditions. The integrated approach was validated and assessed by (a) comparing predicted values with experimental results and (b) benchmarking the model’s outcomes with existing experimental and theoretical findings reported for Mg alloys. This integrated approach offers a cost-effective, efficient pathway for designing advanced Mg alloys while significantly reducing manual input in the alloy development process.
ISSN:2949-8228
2949-8228
DOI:10.1016/j.nxmate.2025.101425