Interpretable physics-informed retrieval-augmented generation language model for end-to-end inorganic crystal synthesis planning
TL;DR
An interpretable Physics-Informed Retrieval-Augmented Generation Language Model for end-to-end inorganic crystal synthesis planning that achieves 91.4% accuracy in synthesis-method prediction and generalizes to materials reported after the knowledge cutoff.
Abstract
Synthesis planning for inorganic materials requires predicting both synthesizability and viable routes by linking microscopic thermodynamic stability with macroscopic synthesis methods, precursors, and processing conditions. Here, we develop an interpretable Physics-Informed Retrieval-Augmented Generation Language Model (PIRAG-LM) for end-to-end inorganic crystal synthesis planning. We construct a material-centered Structured Synthesis Knowledge Base (SSKB) containing route-level records for 13,820 experimentally synthesized inorganic crystals. PIRAG-LM retrieves historical precedents using chemical, structural, and thermodynamic similarity, then employs a structured LLM reasoning module to propose routes, precursors, and processing conditions and assess thermodynamic feasibility, kinetics, and accessibility. It achieves 91.4% accuracy in synthesis-method prediction, compared with 72.1% for the LLM alone, and generalizes to materials reported after the knowledge cutoff. Because the framework relies on retrieval rather than parametric memorization, its performance can be improved by expanding the SSKB without retraining the language model. Guided by PIRAG-LM, we experimentally synthesize five new compounds: BaMo0.3In0.7O2.95, BaNb0.4In0.6O2.9, Hg[B(CN)4]2, CoCo(CN)6, and SrNb2Fe2(PO4)6, via solid-state and solution routes. These results demonstrate an interpretable machine-learning approach that helps bridge computational materials discovery and experimental realization.