Recent advances in design of magnetic functional alloys assisted by machine learning

Magnetic functional alloys (MFAs) constitute a vital category of smart materials, underpinning transformative technologies by the virtue of their emergent multifunctional properties. Nevertheless, the rational design of MFAs continues to pose significant challenges, stemming from expansive compositional space, complex interplay between magnetic behavior and crystal structure, and inherent limitations in conventional experimental and theoretical techniques. In the context of big data, machine learning (ML) has arisen as a revolutionary approach, facilitating the high-throughput discovery of new MFAs with tailored functionalities while simultaneously offering insights into their fundamental physical mechanisms. This review begins by outlining a general framework of ML-driven materials design, covering key algorithms and workflow strategies. Recent breakthroughs in the development of MFAs aided by ML are then highlighted, providing a comprehensive survey of data-driven advances in their design and application. Finally, we discuss persistent challenges and prospects arising from the integration of data-driven methodologies with physics-based models to propel this rapidly evolving field forward.