Language Native Lightly Structured Databases for Large Language Model Driven Composite Materials Research

• URL: http://arxiv.org/abs/2509.06093v2
• Date: Sat, 01 Nov 2025 11:07:08 GMT
• Title: Language Native Lightly Structured Databases for Large Language Model Driven Composite Materials Research
• Authors: Yuze Liu, Zhaoyuan Zhang, Xiangsheng Zeng, Yihe Zhang, Leping Yu, Lejia Wang, Xi Yu,
• Abstract summary: Preparation procedures of materials are often embedded narratively in experimental protocols, research articles, patents, and laboratory notes.<n>We reformulate this challenge into a text-reasoning problem through a framework centered on a text-first, lightly structured materials database.<n>We show how language-native data combined with LLM-based reasoning can significantly accelerate practical material preparation.
• Score: 6.31777560888658
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The preparation procedures of materials are often embedded narratively in experimental protocols, research articles, patents, and laboratory notes, and are structured around procedural sequences, causal relationships, and conditional logic. The synthesis of boron nitride nanosheet (BNNS) polymer composites exemplifies this linguistically encoded decision-making system, where the practical experiments involve interdependent multistage and path-dependent processes such as exfoliation, functionalization, and dispersion, each governed by heterogeneous parameters and contextual contingencies, challenging conventional numerical optimization paradigms for experiment design. We reformulate this challenge into a text-reasoning problem through a framework centered on a text-first, lightly structured materials database and large language models (LLMs) as text reasoning engines. We constructed a database that captures evidence-linked narrative excerpts from the literature while normalizing only the minimum necessary entities, attributes, and relations to enable composite retrieval that unifies semantic matching, lexical cues, and explicit value filters. Building on this language-native, provenance-preserving foundation, the LLM operates in two complementary modes: retrieval-augmented generation (RAG), grounding outputs in retrieved evidence modules from the database, and experience-augmented reasoning (EAR), which leverages iteratively trained text guides derived from multi-source literature-based narrative data as external references to inform reasoning and decision-making. Applying this integration-and-reasoning framework, we demonstrate rapid, laboratory-scale optimization of BNNS preparation, highlighting how language-native data combined with LLM-based reasoning can significantly accelerate practical material preparation.

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