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Magnesium (Mg), being the lightest structural metal, holds immense potential for widespread applications in various fields. The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization. With the rapid advancement of machine learning (ML) technology in recent years, the ``data-driven'' approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys. This paper introduces a novel regression-based Bayesian optimization active learning model (RBOALM) for the development of high-performance Mg-Mn-based wrought alloys. RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges, facilitating the discovery of superior alloy combinations. This model further integrates pre-established regression models as surrogate functions in Bayesian optimization, significantly enhancing the precision of the design process. Leveraging RBOALM, several new high-performance alloys have been successfully designed and prepared. Notably, after mechanical property testing of the designed alloys, the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties, including an ultimate tensile strength of 406 MPa, a yield strength of 287 MPa, and a 23% fracture elongation. Furthermore, the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa, coupled with a remarkable 41% fracture elongation.

• A strategy is proposed to design high-performance Mg–Mn-based wrought Mg alloys based on a novel machine learning model. • The design model innovatively integrates four regression algorithms and Bayesian Optimization algorithm. • The improved Bayesian optimization active learning method outperforms traditional Bayesian optimization in terms of design precision. • Several new high-performance magnesium alloys have been designed and developed using this novel model. Magnesium (Mg), being the lightest structural metal, holds immense potential for widespread applications in various fields. The development of high-performance and cost-effective Mg alloys is crucial to further advancing their commercial utilization. With the rapid advancement of machine learning (ML) technology in recent years, the ''data-driven'' approach for alloy design has provided new perspectives and opportunities for enhancing the performance of Mg alloys. This paper introduces a novel regression-based Bayesian optimization active learning model (RBOALM) for the development of high-performance Mg-Mn-based wrought alloys. RBOALM employs active learning to automatically explore optimal alloy compositions and process parameters within predefined ranges, facilitating the discovery of superior alloy combinations. This model further integrates pre-established regression models as surrogate functions in Bayesian optimization, significantly enhancing the precision of the design process. Leveraging RBOALM, several new high-performance alloys have been successfully designed and prepared. Notably, after mechanical property testing of the designed alloys, the Mg-2.1Zn-2.0Mn-0.5Sn-0.1Ca alloy demonstrates exceptional mechanical properties, including an ultimate tensile strength of 406 MPa, a yield strength of 287 MPa, and a 23% fracture elongation. Furthermore, the Mg-2.7Mn-0.5Al-0.1Ca alloy exhibits an ultimate tensile strength of 211 MPa, coupled with a remarkable 41% fracture elongation. [ABSTRACT FROM AUTHOR]