A property-oriented self-decision design strategy of low-alloyed rare earth-free magnesium alloys with a good strength-ductility synergy based on machine learning
Year: 2025
Type: article
Abstract: Machine learning (ML) is revolutionizing alloy design, yet traditional models face challenges with limited data and complex nonlinearities. Our study presents a self-decision design strategy that integrates target property determination, reverse and forward modeling, and feature importance analysis to optimize low-alloyed rare earth (RE)-free magnesium alloys for strength-ductility synergy. The strategy was validated with experimental data, leading to the development of a new Mg-2Al-1Zn-0.6Ca-0.4Mn (wt%) alloy processed at specific conditions, achieving a tensile strength of 344 MPa and an elongation-to-failure (EL) of 21.3% at room temperature. The discrepancies between experimental and predicted results were less than 5%, underscoring the accuracy of this approach. This streamlined design strategy not only promises to accelerate the development of low-cost, high-performance alloys but also minimizes the need for human intervention, thereby enhancing the efficiency and precision of alloy design.
Source: Journal of Materials Informatics
DOI: 10.20517/jmi.2024.92
Authors: Xu Qin, Qinghang Wang, Xinqian Zhao, Shouxin Xia, Li Wang, Jiabao Long, Yuhui Zhang, Bin Jiang
Institutions: Chongqing University, Yangzhou University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407820555