Automating structure-activity analysis for electrochemical nitrogen reduction catalyst design through multi-agent collaborations

Abstract The electrochemical nitrogen reduction reaction (eNRR) offers sustainable ammonia production, yet elucidating structure-activity relationships (SARs) is challenging. We introduce eNRRCrew, a novel multi-agent framework integrating large language models (LLMs), machine learning, and automated tools to advance eNRR research. By analyzing 2,321 papers, eNRRCrew constructed a comprehensive database of electrocatalyst properties, conditions, and performance. The framework employs a random forest classifier for eNRR yield prediction, with model interpretability analysis revealing key factors like space group number and elemental electronegativity difference. Additionally, clustering analysis identifies distinct Faradaic efficiency patterns. eNRRCrew's five LLM agents enable natural language interaction for novel catalyst recommendation, performance prediction, data analysis, and literature insights. This approach surpasses traditional methods in extracting SARs and guiding rational catalyst design, offering a scalable platform for various electrocatalysis domains and a new paradigm for LLM-driven scientific discovery.