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

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.