Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model

Materials synthesis procedures are predominantly documented as narrative text in protocols and lab notebooks, rendering them inaccessible to conventional structured data optimization. This language-native nature poses a critical challenge for complex, multistage processes--such as the preparation of boron nitride nanosheet (BNNS)--where outcomes depend on path-dependent choices in exfoliation and functionalization. Here, we recast synthesis planning as a text reasoning task enabled by a lightly structured text database, which preserves the conditional logic and causal contexts essential for expert-like decision-making. Building on a heterogeneous schema that indexes both narrative excerpts and computable entities (e.g., reaction conditions), our system implements a hybrid retrieval engine to combine semantic context with precise parameter filtering. On top of this, the framework operates in two modes, i.e. retrieval-augmented generation (RAG), which grounds recommendations in retrieved evidence modules, and experience-augmented reasoning (EAR), which uses iteratively refined text guides distilled from multi-source narrative data. Instead of suggesting single "optimal" settings, the system produces interpretable guidance aligned with expert reasoning patterns--hypotheses, parameter ranges, and citation-backed standard operating procedures--that support iterative planning and failure diagnosis. We validated this framework on the targeted exfoliation of BNNS, a process highly sensitive to multivariate constraints. The system successfully identified optimal combinations of grinding aids, milling configurations, and separation strategies from a wide range of literature-reported methods, which were experimentally verified to yield high-quality nanosheets, illustrating the potential of language-native reasoning to streamline critical operations in materials processing.