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 |
| 卷 | 61 |
| 發行號 | 70 |
| DOIs | |
| 出版狀態 | Published - 26 8月 2025 |
指紋
深入研究「ChemReactSeek: an artificial intelligence-guided chemical reaction protocol design using retrieval-augmented large language models」主題。共同形成了獨特的指紋。引用此
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver