Identifying reliable synthesis pathways in materials chemistry is a complex task, particularly in polymer science, due to the intricate and often non-unique nomenclature of macromolecules. To address this challenge, we propose an agent system that integrates large language models (LLMs) and knowledge graphs (KGs). By leveraging LLMs' powerful capabilities for extracting and recognizing chemical substance names, and storing the extracted data in a structured knowledge graph, our system fully automates the retrieval of relevant literatures, extraction of reaction data, database querying, construction of retrosynthetic pathway trees, further expansion through the retrieval of additional literature and recommendation of optimal reaction pathways. A novel Multi-branched Reaction Pathway Search (MBRPS) algorithm enables the exploration of all pathways, with a particular focus on multi-branched ones, helping LLMs overcome weak reasoning in multi-branched paths. This work represents the first attempt to develop a fully automated retrosynthesis planning agent tailored specially for macromolecules powered by LLMs. Applied to polyimide synthesis, our new approach constructs a retrosynthetic pathway tree with hundreds of pathways and recommends optimized routes, including both known and novel pathways, demonstrating its effectiveness and potential for broader applications.

本研究针对材料化学，特别是聚合物科学中，识别可靠合成路径的复杂性问题，提出了一种将大型语言模型（LLMs）和知识图谱（KGs）集成的代理系统。通过采用多分支反应路径搜索（MBRPS）算法，该系统有效自动化了文献检索、反应数据提取与逆合成路径树的构建，显示出在聚酰亚胺合成中实现了数百条路径的最佳推荐，展示了其有效性及广泛应用的潜力。

利用大型语言模型作为知识驱动的代理进行可靠的逆合成规划