Abstract
Neodymium-Iron-Boron (NdFeB) magnets, renowned for their exceptional magnetic properties, are pivotal in modern technologies ranging from electric vehicles to wind turbines. The optimization of their chemical composition—a delicate balance of neodymium (Nd), iron (Fe), boron (B), and rare-earth additives like dysprosium (Dy)—is critical for enhancing performance while reducing costs and environmental impact. Traditional trial-and-error methods for formula development are time-consuming and resource-intensive. This paper explores how machine learning (ML), a cornerstone of materials informatics, can revolutionize the prediction of new NdFeB magnet formulas by leveraging multi-scale data integration, advanced modeling techniques, and interpretability frameworks. We discuss the challenges, methodologies, and recent breakthroughs in this field, culminating in a roadmap for ML-driven materials discovery.