A framework of active data selection and quantum-enhanced regression for predicting magnetic properties of sintered NdFeB magnets

Abstract Sintered neodymium-iron-boron (NdFeB) magnets are indispensable in high-performance applications, but their optimization is challenged by complex structure-property relationships and limited data. In this work, we curate the first multi-domain database for this system (1994 industrial and academic samples) and systematically evaluate active learning (AL) strategies on classical and quantum-enhanced regressors. First, our “domain-aware” analysis reveals quantitative differences in design heuristics between industrial and academic data. Second, we present a methodological blueprint for integrating quantum kernel regression into an AL framework using a bootstrapped ensemble for uncertainty quantification. Finally, and most significantly, our results reveal AL effectiveness is strongly model-dependent. Its advantage ranges from significant acceleration (Random Forest, SVR) to being diminished (XGBoost), or even inverted—proving detrimental compared to random sampling—as shown in our quantum-enhanced SVR case study. This finding provides critical new insights for the strategic application of machine learning in materials discovery.