Data-driven design of high-abundance rare earth permanent magnets for low-carbon and resource-efficient production

Rare-earth permanent magnets are essential for renewable energy and electric vehicle technologies, but their dependence on critical rare-earth elements raises concerns regarding resource security, cost, and environmental impact. Here, we develop an integrated data-driven framework for the sustainable design of high-abundance rare-earth permanent magnets. A dataset containing 346 experimental samples was constructed to establish composition-property relationships for Br, H_cj, and (BH)_max. Eight machine learning algorithms were compared, and the multi-layer perceptron (MLP) model exhibited the most balanced overall performance after model refinement. The optimized MLP achieved testing R² values of 0.979, 0.901, and 0.955 for Br, H_cj, and (BH)_max respectively, and repeated five-fold cross-validation supported its robustness. SHAP analysis indicated that Nd exerted the strongest statistical influence on the predicted magnetic properties, while Ce and La also contributed through nonlinear composition-property associations. The optimized MLP model was subsequently integrated with NSGA-II and TOPSIS to balance predicted (BH)_max, material cost, and a composition-related upstream global-warming-potential(GWP) indicator. The selected Ce-rich candidate achieved a predicted (BH)_max of 35.16 MGOe, an estimated material cost of 4.78 $/kg, and a GWP indicator of 16.41 kg CO₂-eq/kg. These results demonstrate the potential of interpretable machine learning combined with multi-objective optimization for screening resource-efficient permanent-magnet compositions.