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.

翻译：材料合成流程大多以叙述性文本形式记录于论文、方案和实验记录中，使其难以融入传统数据驱动的优化框架。这种语言原生特性对氧化硼纳米片（BNNS）制备等复杂多阶段工艺构成特殊挑战——此类工艺的最终结果取决于剥离、功能化及功能化修饰过程中路径依赖的决策选择。我们提出将材料合成规划重构为文本推理问题，通过构建轻结构化知识基底保留工艺逻辑与因果语境，同时暴露可计算元素供检索。基于该表征框架，我们整合语义匹配、词汇搜索与参数感知过滤技术，支撑检索增强生成系统输出更精准、根基更扎实的合成指导。进一步引入经验增强推理机制：通过多源叙事文本迭代精炼的推理指引，支持假设生成、故障诊断与方案修订。在BNNS定向剥离这一受多变量约束且文献方案跨实验室迁移性有限的合成问题中验证本框架。通过将分散的文献证据与实验观测的失效模式相结合，系统仅经过三轮迭代即收敛至高性能方案，产出符合目标规格的高质量超薄纳米片，显著缩短了通常依赖专家反复试错的漫长周期。该框架通过实现面向工艺知识的语言原生推理，推动AI从文献辅助工具向主动合成规划、自适应调整及复杂材料工作流加速的方向演进。