Interpretable physics-informed retrieval-augmented generation language model for end-to-end inorganic crystal synthesis planning
This paper presents PIRAG-LM, an interpretable, physics-informed retrieval-augmented generation model that leverages a structured knowledge base of over 13,000 experimental records to accurately predict and plan end-to-end inorganic crystal synthesis routes, successfully guiding the experimental realization of five new compounds.