GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design
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.
698af8e01b2dc6b37d61b34a
Isabella A. Stewart
698af8e01b2dc6b37d61b34b
Tarjei Paule Hage
698af8e01b2dc6b37d61b34c
Yu-Chuan Hsu
698af8e01b2dc6b37d61b34d
Markus J. Buehler
claimed_verified
2026-02-10T09:27:39.258Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/623ce1c6b66fedf374859fe7/lhbMLg6BxLCb9DD4rgjfx.jpeg
Markus Buehler
mjbuehler
user
mjbuehler
GraphAgents: Knowledge Graph-Guided Agentic AI for Cross-Domain Materials Design
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.
A multi-agent framework guided by knowledge graphs addresses materials science challenges by integrating specialized agents for problem decomposition, evidence retrieval, and graph traversal to discover sustainable PFAS alternatives.
large language models multi-agent framework knowledge graphs materials science problem decomposition evidence retrieval graph traversal hypothesis generation ablation studies domain-spanning reasoning sustainable substitutes PFAS chemicals tribological performance thermal stability chemical resistance biocompatibility