Artificial Intelligence Assisted Design of Unclonable High-Entropy Phosphors

High entropy materials (HEMs) are a rapidly evolving class of materials, offering interesting properties for catalysis, energy storage, or optoelectronics. However, their photoluminescence properties have been less explored. In this context, the project AI-Unclon aims to use machine learning (ML) tools to predict how the photoluminescence colors of high-entropy phosphors change with variations in composition, opening doors to innovative applications in solid-state lighting, thermometry and telecommunications. As a proof of concept, a synthesis method assisted by machine learning will be developed in order to design phosphors materials optimized for solid-state lighting applications.