Illustration of different approaches in searching for optimal synthesis conditions, along with the performance of each approach on the electrochemical oxidation of α-pinene to verbenone, with "n" indicating the number of reactions per iteration. The combination of LLM and ML leverages both domain knowledge in chemistry and statistical learning. (B) Selected substrates based on ML predictions for reactivity and selectivity, along with the resulting optimization process and yields. Substrates are grouped by interpretable substructures and reaction yields. Human-level performance was estimated by reproducing conditions from literature or manually optimizing new reactions (Table ). Each reaction batch size was 4, with a total of 88 reactions per substrate over 10 iterations plus initialization.