Individual descriptor categories were submitted to PCA and the resulting first principal component of each of these parameter subsets was plotted to generate the 3D map shown in Fig. (for more details of the parameter selection, see SI). To illustrate the map's interpretability, we evaluated four representative bisphosphine ligands (L1 -L4) in our designer map as well as in a conventional PCA map (Fig. ). From the ligand location in the designer interpretable PCA map, one can glean more detailed information when compared to a conventional PCA map. This is best illustrated when one plots 2D cross-sections of the 3D space (Fig. , steric/electronic, geometric/steric, and geometric/electronic representations). Specifically, differences in ligand structure can be readily assigned to physically meaningful ligand attributes. For instance, phosphite L1 is similar to L3 in terms of sterics but has a significantly different electronic profile. Likewise, DTBM-Segphos L4 is similar to L2 in terms of electronics and geometric features, but the key differentiating factor is sterics. As showcased below, the designer maps can be applied to various downstream data science steps in multi-objective optimization campaigns. In addition to the reaction optimization applications presented (vide infra), it can be envisioned that this calculated parameter library and chemical space map could be used for many other potential applications such as training set design, novel ligand generation, and mechanistic understanding.