{"ID":2873796,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06093","arxiv_id":"2509.06093","title":"Language-Native Materials Processing Design by Lightly Structured Text Database and Reasoning Large Language Model","abstract":"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.","short_abstract":"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 nanos...","url_abs":"https://arxiv.org/abs/2509.06093","url_pdf":"https://arxiv.org/pdf/2509.06093v3","authors":"[\"Yuze Liu\",\"Zhaoyuan Zhang\",\"Xiangsheng Zeng\",\"Yihe Zhang\",\"Leping Yu\",\"Liu Yang\",\"Lejia Wang\",\"Xi Yu\"]","published":"2025-09-07T15:15:55Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cond-mat.mtrl-sci\",\"cs.AI\",\"cs.CL\"]","methods":"[\"RAG\",\"Language Model\"]","has_code":false}