As the first stage, we designed an HTE screening campaign that assessed a combinatorial matrix of catalysts and substrates. The resultant quantity and diversity of data would allow for construction of enantioselectivity correlations with substrate and catalyst structural features through statistical modeling. By assessing a combinatorial matrix, we hypothesized that most general features required for effective asymmetric catalysis would be revealed and serve to seed further computational and mechanistic studies required to elucidate the mechanism of this reaction. Ten representative catalysts that sampled two points of modulation, the phosphinate and quinoline substituents, were selected, as they showcased a diversity of responses in the initial optimization campaign. These catalysts were evaluated against the 17 sulfonimidamide substrates that provided reasonable yields in our initial scope evaluation (Figure ). The 170 reactions exhibited a well-distributed and wide range of observed selectivity ranging from 53:47 to 99:1 er, with 51-100% conversions. The matrix approach highlights that each catalyst reacts uniquely with every substrate and vice versa, positioning this dataset well for statistical modeling to decipher catalystsubstrate interactions that impact selectivity.