From Explainable Machine Learning to Physics-Guided Design Rules for Inorganic Phosphors
Abstract
Abstract The rational design of inorganic phosphors with targeted emission wavelengths is challenging because luminescence depends on strongly coupled structural, electronic, and excitation-dependent factors. Machine-learning studies in this area often prioritize predictive accuracy without systematically evaluating descriptor reproducibility and physical interpretability. Here, we develop a physics-guided explainable machine-learning framework that combines multi-method feature selection, descriptor stability analysis, and physicochemical interpretation for emission-wavelength prediction. A curated dataset of 2327 single-dopant phosphor compositions, initially represented by 125 physicochemical descriptors, was refined to 46 physically meaningful descriptors. Twelve complementary feature-selection methods were evaluated using eight stability metrics and 10-fold cross-validation. Pearson correlation and ANOVA-F, which are mathematically related univariate criteria for measuring linear association, identified the same seven-descriptor subset with complete 10/10 fold-level reproducibility. The resulting Random Forest model achieved a mean cross-validated R 2 = 0.9308, MAE = 7.78 nm, and RMSE = 22.55 nm. The seven-descriptor model retained essentially the same predictive performance as the best fully reproducible 24-descriptor model while reducing descriptor dimensionality by about 71% relative to that model and by about 86% relative to the full physically meaningful candidate…
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