Unravelling the evolution of nickel-catalyzed C–O bond activation with data-driven strategies

Abstract

Since the 1970s, nickel has proven to be an exceptionally efficient catalyst for cross-coupling reactions, particularly in the activation of C–O bonds, which serves as an environmentally friendly alternative to organic halides. The relentless exploration by chemists of the synthetic methodologies and mechanisms of this field has progressively fostered the emergence of an increasingly mature yet intricate discipline. Despite its apparent complexity, the core patterns remain hidden within some significant works. The development of large language models (LLMs) has provided unprecedented opportunities to navigate this complex landscape and uncover hidden patterns. Here, we introduce GPT-NiCOBot, a modular platform that integrates LLMs with chemistry-specific tools to autonomously extract reactions and identify key patterns in reagents and catalysts from peer-reviewed papers. Moreover, by combining the core citation network with in-depth chemical knowledge, this platform constructs a more effective and comprehensive research assistance framework. This system demonstrates the potential of LLMs to accelerate research in nickel catalysis and suggests broader applications in other chemical subfields.