Workshops Archive

LABMATE: Language Model Based Multi-Agent System to Accelerate Catalysis Experiments

Workshop: Frontiers in Generative AI for HPC Science and Engineering: Foundations, Challenges, and Opportunities

Authors: Anurag Acharya (Pacific Northwest National Laboratory (PNNL)); Anshu Kiran Sharma (Florida International University, Pacific Northwest National Laboratory (PNNL)); and Derek Parker, Timothy Vega, Rizwan A. Ashraf, Natalie M. Isenberg, Jan Strube, and Robert Rallo (Pacific Northwest National Laboratory (PNNL))

Abstract: Large Language Models (LLMs) are unreliable to make decisions due to their potential to hallucinate, and unable to perform complex tasks like running simulations essential to fields like Material Science. We introduce LABMATE (LAnguage model Based Multi-agent system to Accelerate caTalysis Experiments), a human-in-the-loop copilot framework that utilizes LLM agents to make catalysis research faster. LABMATE allows human experts to run simulations, track particle sizes, run data analysis, conduct literature review, and generate potential hypothesis all in one framework, thereby expediting the research process. When evaluated on the major benchmarks, LABMATE performs comparable to or better than most frontier LLMs, showing that in addition to accelerating the experimental process, our framework is also on par in domain knowledge compared to using a simple LLM. Furthermore, since the core architecture of the system is domain-agnostic, it can easily be adapted to other domains.

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