The C-N benchmark presents trade-offs in the objectives. As shown in Figure , increasing yield often results in an increase in cost. This series of optimal trade-offs between yield and cost is called , the optimal catalyst complex is AlPhos, in line with results from Baumgartner et al. However, it is clear that some conditions result in a prediction of yield being greater than 100%. Some of the original data has yield greater than 100%, apparently due to small errors in HPLC measurements. Since the data-driven model has no concept of chemistry (i.e., that yield cannot be greater than 100%), it simply learns this pattern and extrapolates it. The lack of physical constraints is a known issue in data-driven models and could possibly be overcome through hybrid models that combine physical insights and learned approximations. 3 Conclusions