Construction of an artificial-intelligence agent for the discovery of next-generation white-LED phosphors

Large language models have been extensively employed for scientific research from different aspects, yet their performance is often limited by gaps in highly specialized knowledge. To bridge this divide, in this perspective we take phosphor materials for white LED applications as a model system and construct a domain-specific knowledge base that couples retrieval-augmented generation with a numerical-querying model context protocol. By automatically extracting and structuring data from more than 5400 publications-including chemical compositions, crystallographic parameters, excitation-emission wavelengths, and synthesis conditions-we construct an artificial-intelligence agent that delivers both broad semantic search and exact parameter lookup, each answer accompanied by verifiable references. This hybrid approach mitigates hallucinations, and improves recall and precision in expert-level question-answering. Finally, we outline how linking this curated corpus to lightweight machine-learning models and even automated experimental synthesis facilities can close the loop from target specification to experimental validation, offering a blueprint for accelerated materials discovery.