This research demonstrates how Large Language Models (LLMs) can extract and utilize quantitative chemical knowledge to accelerate Bayesian optimization of chemical reactions, especially when limited experimental data is available.

• Combines LLM capabilities with traditional Bayesian optimization techniques
• Enables more efficient transfer learning from pre-existing chemical information
• Significantly improves optimization performance in low-data regimes
• Creates a pathway for LLMs to contribute quantitative value in chemical engineering applications

This innovation matters for Engineering as it provides a practical approach to overcome data limitations in chemical process optimization, potentially reducing experimental costs and accelerating discovery timelines for new materials and reactions.

Original Paper: Distilling and exploiting quantitative insights from Large Language Models for enhanced Bayesian optimization of chemical reactions