{"ID":2872686,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.08736","arxiv_id":"2509.08736","title":"ChemBOMAS: Accelerated BO in Chemistry with LLM-Enhanced Multi-Agent System","abstract":"Bayesian optimization (BO) is a powerful tool for scientific discovery in chemistry, yet its efficiency is often hampered by the sparse experimental data and vast search space. Here, we introduce ChemBOMAS: a large language model (LLM)-enhanced multi-agent system that accelerates BO through synergistic data- and knowledge-driven strategies. Firstly, the data-driven strategy involves an 8B-scale LLM regressor fine-tuned on a mere 1% labeled samples for pseudo-data generation, robustly initializing the optimization process. Secondly, the knowledge-driven strategy employs a hybrid Retrieval-Augmented Generation approach to guide LLM in dividing the search space while mitigating LLM hallucinations. An Upper Confidence Bound algorithm then identifies high-potential subspaces within this established partition. Across the LLM-refined subspaces and supported by LLM-generated data, BO achieves the improvement of effectiveness and efficiency. Comprehensive evaluations across multiple scientific benchmarks demonstrate that ChemBOMAS set a new state-of-the-art, accelerating optimization efficiency by up to 5-fold compared to baseline methods.","short_abstract":"Bayesian optimization (BO) is a powerful tool for scientific discovery in chemistry, yet its efficiency is often hampered by the sparse experimental data and vast search space. Here, we introduce ChemBOMAS: a large language model (LLM)-enhanced multi-agent system that accelerates BO through synergistic data- and knowle...","url_abs":"https://arxiv.org/abs/2509.08736","url_pdf":"https://arxiv.org/pdf/2509.08736v2","authors":"[\"Dong Han\",\"Zhehong Ai\",\"Pengxiang Cai\",\"Shanya Lu\",\"Jianpeng Chen\",\"Zihao Ye\",\"Shuzhou Sun\",\"Ben Gao\",\"Lingli Ge\",\"Weida Wang\",\"Xiangxin Zhou\",\"Xihui Liu\",\"Mao Su\",\"Wanli Ouyang\",\"Lei Bai\",\"Dongzhan Zhou\",\"Tao Xu\",\"Yuqiang Li\",\"Shufei Zhang\"]","published":"2025-09-10T16:24:08Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}