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

Abstract

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, Hcj, and (BH)max. Eight machine learning (ML) 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, Hcj, and (BH)max, respectively, and repeated five-fold cross-validation supported its robustness. SHapley Additive exPlanations 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 non-dominated sorting genetic algorithm II and Technique for Order Preference by Similarity to Ideal Solution 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 ML combined with multi-objective optimization for screening resource-efficient permanent-magnet compositions.