Reshaping MOFs text mining with a dynamic multi-agents framework of large language model

• URL: http://arxiv.org/abs/2504.18880v2
• Date: Fri, 25 Jul 2025 10:08:19 GMT
• Title: Reshaping MOFs text mining with a dynamic multi-agents framework of large language model
• Authors: Zuhong Lin, Daoyuan Ren, Kai Ran, Jing Sun, Songlin Yu, Xuefeng Bai, Xiaotiang Huang, Haiyang He, Pengxu Pan, Xiaohang Zhang, Ying Fang, Tianying Wang, Minli Wu, Zhanglin Li, Xiaochuan Zhang, Haipu Li, Jingjing Yao,
• Abstract summary: We present MOFh6, a large language model (LLM)-based multi-agent system designed to extract, structure, and apply synthesis knowledge.<n>MoFh6 achieves 99% accuracy in synthesis data parsing and resolves 94.1% of complex co-reference abbreviations.<n>It processes a single full-text document in 9.6 seconds and localizes structured synthesis descriptions within 36 seconds, with the cost per 100 papers reduced to USD 4.24, a 76% saving over existing systems.
• Score: 5.150905688058796
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately identifying synthesis conditions for metal-organic frameworks (MOFs) remains a critical bottleneck in materials research, as translating literature-derived knowledge into actionable insights is hindered by the unstructured and heterogeneous nature of scientific texts. Here we present MOFh6, a large language model (LLM)-based multi-agent system designed to extract, structure, and apply synthesis knowledge from diverse input formats, including raw literature and crystal codes. Built on gpt-4o-mini and fine-tuned with up to few-shot expert-annotated data, MOFh6 achieves 99% accuracy in synthesis data parsing and resolves 94.1% of complex co-reference abbreviations. It processes a single full-text document in 9.6 seconds and localizes structured synthesis descriptions within 36 seconds, with the cost per 100 papers reduced to USD 4.24, a 76% saving over existing systems. By addressing long-standing limitations in cross-paragraph semantic fusion and terminology standardization, MOFh6 reshapes the LLM-based paradigm for MOF synthesis research, transforming static retrieval into an integrated and dynamic knowledge acquisition process. This shift bridges the gap between scientific literature and actionable synthesis design, providing a scalable framework for accelerating materials discovery.

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