This project is focused on enhancing the efficiency of the Suzuki reaction process through an advanced multi-agent system, incorporating large language models (LLMs) and Bayesian Optimization (BO). The innovation lies in the employment of specialized sub-agents, each with expertise in a crucial domain of the reaction: catalyst design, solvent effects, and base selection. These agents work in concert with a supervisory agent, which integrates their insights and findings. This collaborative framework aims to optimize reaction conditions iteratively, leveraging both prior knowledge and experimental data to navigate the chemical space effectively.

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References:

1. Perera, Damith, et al. “A platform for automated nanomole-scale reaction screening and micromole-scale synthesis in flow.” Science 359.6374 (2018): 429-434.
2. Guo, Taicheng, et al. “Large language model based multi-agents: A survey of progress and challenges.” arXiv preprint arXiv:2402.01680 (2024).
3. Liu, Tennison, et al. “Large Language Models to Enhance Bayesian Optimization.” arXiv preprint arXiv:2402.03921 (2024).