A Review of Artificial Intelligence‐Driven Innovations in Soft Magnetic Materials Optimization: Current Trends and Future Horizons

With the continuous accumulation of data, machine learning is playing an increasingly important role in materials science, especially demonstrating significant advantages in predicting material compositions and developing new alloy systems for soft magnetic materials. However, currently, it mainly focuses on composition optimization while often neglecting the impact of structure and fundamental physical parameters. On this matter, we have discussed model selection in machine learning modeling, the issues encountered, and the limitations of the composition‐focused approach. Through insights gained from research in other material fields, it is highlighted that integrating machine learning with other computational methods such as first‐principles calculations and phase diagram computations can significantly enhance the predictive capabilities of machine learning. We analyzed these AI‐enhanced cases and highlighted how they have the potential to lead to further breakthroughs in soft magnetic materials.