Pre-trained knowledge elevates large language models beyond traditional chemical reaction optimizers

Understand

Modern optimization in experimental chemistry employs algorithmic search through black-box parameter spaces.

• Here we demonstrate that pre-trained knowledge in large language models (LLMs) fundamentally changes this paradigm.
• Using six fully enumerated categorical reaction datasets (768-5,684 experiments), we benchmark LLM-guided optimization (LLM-GO) against Bayesian optimization (BO) and random sampling.
• Frontier LLMs consistently match or exceed BO performance across five single-objective datasets, with advantages growing as parameter complexity increases and high-performing conditions become scarce (<5% of space).