A Stacking Ensemble Model for Improving the Prediction Performance of Luminescent Color of Phosphor

Determining the luminescent colors of phosphors used in display and lighting applications is a crucial step in discovering new functional luminescent materials. This study collects the experimental conditions for Ca₁₁(SiO₄)₄(BO₃)₂ (CSB) phosphor to produce different colors. Through 12 commonly used machine learning models and stacking ensemble model, the luminescence colors of CSB with different ion doping are accurately predicted, and the reliability of the results through experiments is verified. The findings demonstrate that the stacking ensemble model can effectively improve forecasting performance compared to a single optimal model, with overall accuracy, precision, recall score, and f1 score of 98.19%, 98.27%, 98.10%, and 98.18%, respectively. It is the best stacking ensemble model currently known. Compared with the single best classification model, the stacking ensemble model achieves relative improvements of ≈3.55%, 2.99%, 3.46%, and 3.89%, respectively. In addition, the Commission Internationale de L'Eclairage (CIE)-chromaticity diagram of the luminescence color of phosphors is successfully predicted by using a clustering method applied to the output of the stacking model; and experiments further verify the generalization performance of the model. The research results reveal that the stacking ensemble model has high precision and speed in predicting phosphor luminescence colors, and has great potential in optimizing luminescence properties.