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 papers, protocols, and laboratory records, placing them beyond the reach of conventional data-driven optimization frameworks. This language-native character poses a particular challenge for complex, multistage processes such as the preparation of boron nitride nanosheets (BNNS), where outcomes depend on path-dependent choices in exfoliation, functionalization, and functionalization. Here, we recast synthesis planning of the materials as a text reasoning problem enabled by a lightly structured knowledge substrate that preserves the procedural logic and causal contexts while exposing computable elements for retrieval. Built on this representation, our framework combines semantic matching, lexical search, and parameter-aware filtering to support retrieval-augmented generation with more accurate and better-grounded synthesis guidance. We further introduce experience-augmented reasoning, in which iteratively refined text guides distilled from multi-source narratives support hypothesis generation, failure diagnosis, and protocol revision. We validated the framework in the targeted exfoliation of BNNS, a synthesis problem governed by multivariate constraints and limited transferability of literature protocols across laboratory settings. By integrating dispersed literature evidence with experimentally observed failure modes, the system converged within only three iterative rounds on a high-performing protocol that yielded high-quality ultrathin nanosheets meeting the target specifications, substantially shortening what is often a prolonged cycle of expert-led trial-and-error. By enabling language-native reasoning over procedural knowledge, this framework moves AI beyond literature assistance toward active synthesis planning, adaptation and acceleration in complex materials workflows.