Phosphor Materials: Machine Learning Approach: Fundamentals, Data Processing & Photoluminescence Prediction - Tapa blanda

Sinopsis

Phosphor materials are important luminescent materials whose photoluminescence properties are influenced by host composition, activator and sensitizer ions, crystal structure, electronic configuration, dopant concentration, and measurement conditions. Therefore, a systematic understanding of material properties and photoluminescence mechanisms is essential for studying and predicting excitation and emission wavelengths.This book has presented the fundamental concepts of phosphor materials and photoluminescence, including excitation, emission, Stokes shift, energy-level transitions, and the major factors affecting luminescence behavior. It has also discussed important material descriptors such as ionic radius, atomic radius, electronegativity, valence electrons, electronic configuration, and structural properties. These descriptors provide a suitable foundation for representing phosphor materials as structured feature vectors for machine-learning applications. Thus, combining phosphor science, material descriptors, and data-driven approaches can support the efficient prediction and development of advanced luminescent materials.

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