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ChemReactSeek: an artificial intelligence-guided chemical reaction protocol design using retrieval-augmented large language models

  • Ziyang Gong
  • , Chengwei Zhang
  • , Danyang Song
  • , Weida Xia
  • , Bin Shen
  • , Weike Su
  • , Hongliang Duan
  • , An Su
  • Zhejiang University of Technology

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

We introduce ChemReactSeek, an advanced artificial intelligence platform that integrates retrieval-augmented generation using large language models (LLMs) to automate the design of chemical reaction protocols. The system employs DeepSeek-v3 to extract and structure data from scientific literature, enabling the construction of a specialized knowledge base focused on hydrogenation reactions. By combining FAISS-based semantic search with LLM-driven reasoning, ChemReactSeek generates executable reaction conditions, which we further validate through experiments on heterogeneous hydrogenation.

原文English
頁(從 - 到)13137-13140
頁數4
期刊Chemical Communications
61
發行號70
DOIs
出版狀態Published - 26 8月 2025

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