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Interpretable machine learning for Curie temperature prediction of magnetic materials: compositional descriptors, shap analysis, and a perovskite case study

The Northeast Materials Database is leveraged to develop machine learning models that predict magnetic materials with targeted Curie temperatures from composition-derived descriptors rooted in molecular-level elemental properties, supplemented by a small set of coarse crystal-system and structure-family indicators.