Exploring deep learning and machine learning for novel red phosphor materials

In the pursuit of enhancing red phosphor materials, integrating Deep Learning (DL) and machine Learning (ML) techniques has emerged as a transformative avenue. Challenges persist, necessitating comprehensive exploration and detailed comparative analysis of methods, focusing on predictive accuracy, interpretability, and computational demands. The role of regression models and their coefficients in material property prediction requires in-depth investigation. A systematic approach was employed, leveraging literature reviews and comparative analyses. Relevant articles were meticulously selected, focusing on methodologies and algorithms in predicting material properties. The study aimed to explore the integration of DL and ML in advancing red phosphor materials, evaluating algorithms and seven different regression models. Linear Regression, Robust Regression, and Lasso Regression emerged as top-performing models in predicting red phosphor material properties, specifically the 2E energy of Mn4+ doped crystals, supported by comprehensive coefficient analysis. This research offers valuable insights, informing the selection of models for specific tasks and optimizing the integration of DL and ML techniques in the field of red phosphor materials.