图代理：基于知识图谱的跨领域材料设计智能体系统

摘要

大型语言模型（LLMs）有望通过不断扩展的科学领域进行推理来加速科学发现。然而，当前的挑战已不再是信息获取，而是如何以具有意义的、跨领域的方式建立信息关联。在材料科学领域，创新需要整合从分子化学到机械性能的多维度概念，这一挑战尤为突出。无论是人类研究者还是单智能体LLMs都难以完全应对这种信息洪流，后者还常常出现幻觉问题。为突破这一瓶颈，我们提出了一种基于大规模知识图谱的多智能体框架，用于寻找全氟和多氟烷基物质（PFAS）的可持续替代品——这类化学物质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，完整的多智能体流程优于单次提示方式，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，系统可在聚焦领域关键结果的利用性搜索与发现新兴跨领域连接的探索性搜索之间灵活切换。以生物医学导管为例，该框架成功生成了一系列平衡摩擦学性能、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.

图代理：基于知识图谱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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