The identification of optimal ligands HexaMeO-BIPHEP L12 and Walphos L13 highlights several notable strengths of this approach. First, the optimization of the Hayashi-Heck reaction demonstrates the ability of the statistical models to predict performance improvements resulting from subtle changes in ligand structure (Fig. ). Comparing HexaMeO-BIPHEP L12 to ligands L5, L8, L14, and L15, it would be difficult to intuitively predict that one ligand would perform significantly better than the others as all share similar C2-biaryl backbones decorated with electrondonating substituents. Nevertheless, the statistical model correctly identified L12 as a highperforming ligand for the Hayashi-Heck reaction. While L12 has been known since 1991, there have only been two reports of its use in the subsequent peer reviewed literature. Therefore, this strategy led to the identification of a seldom-used ligand that would not typically be included in a conventional optimization. Additionally, our modeling workflow enables "scaffold hopping"-the prediction of optimized ligands with distinct backbones (Fig. ). This was critical to the successful improvement of the hydroformylation reaction, as all readily available derivatives of (R,R)-Ph-BPE L6 had been previously tested and did not demonstrate improved performance.