Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 13137-13140 |
| Number of pages | 4 |
| Journal | Chemical Communications |
| Volume | 61 |
| Issue number | 70 |
| DOIs | |
| Publication status | Published - 26 Aug 2025 |