GraphAgents：基于知识图谱引导的跨领域材料设计智能体系统

摘要

大型语言模型（LLMs）有望通过跨领域推理加速科学发现进程。然而当前挑战已非信息获取，而在于如何建立跨学科的有效关联。这在材料科学领域尤为突出——该领域的创新需要融合从分子化学到机械性能的多维度概念。无论是人类研究者还是单智能体LLM都难以完全应对这种信息洪流，后者还常出现幻觉现象。为突破此瓶颈，我们提出一种基于大规模知识图谱的多智能体框架，用于寻找全氟/多氟烷基物质（PFAS）的可持续替代品——这类化学品正面临严格的监管审查。该框架中的智能体分别专注于问题分解、证据检索、设计参数提取和图谱遍历，通过发掘不同知识模块间的潜在联系来支持假设生成。消融实验表明，完整多智能体流程优于单次提示方法，印证了分布式专业化与关联推理的价值。我们证明通过定制图谱遍历策略，系统可在聚焦关键性能的利用式搜索与发现新兴跨领域连接的探索式搜索间动态切换。以生物医学导管为例，该框架成功生成兼具摩擦学性能、热稳定性、耐化学性与生物相容性的可持续无PFAS替代方案。本研究建立了知识图谱与多智能体推理相结合的材料设计新范式，并通过多个初步设计方案验证了该方法的可行性。

English

Large Language Models (LLMs) promise to accelerate discovery by reasoning across the expanding scientific landscape. Yet, the challenge is no longer access to information but connecting it in meaningful, domain-spanning ways. In materials science, where innovation demands integrating concepts from molecular chemistry to mechanical performance, this is especially acute. Neither humans nor single-agent LLMs can fully contend with this torrent of information, with the latter often prone to hallucinations. To address this bottleneck, we introduce a multi-agent framework guided by large-scale knowledge graphs to find sustainable substitutes for per- and polyfluoroalkyl substances (PFAS)-chemicals currently under intense regulatory scrutiny. Agents in the framework specialize in problem decomposition, evidence retrieval, design parameter extraction, and graph traversal, uncovering latent connections across distinct knowledge pockets to support hypothesis generation. Ablation studies show that the full multi-agent pipeline outperforms single-shot prompting, underscoring the value of distributed specialization and relational reasoning. We demonstrate that by tailoring graph traversal strategies, the system alternates between exploitative searches focusing on domain-critical outcomes and exploratory searches surfacing emergent cross-connections. Illustrated through the exemplar of biomedical tubing, the framework generates sustainable PFAS-free alternatives that balance tribological performance, thermal stability, chemical resistance, and biocompatibility. This work establishes a framework combining knowledge graphs with multi-agent reasoning to expand the materials design space, showcasing several initial design candidates to demonstrate the approach.

GraphAgents：基于知识图谱引导的跨领域材料设计智能体系统

GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design

摘要

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