Although our ML models perform well at predicting whether or not a ligand will demonstrate hemilability in the context of CSD structures, validating these predictions in the context of homogenous catalysis would require difficult experiments. As an alternative strategy to validate our models, we carried out electronic structure calculations with density functional theory (DFT) to discern differences in ligand dissociation energies between our hemilabile and nonhemilabile (i.e., either from the small or semi-supervised) ligands. We selected 100 total tridentate ligands that were neutral (i.e., to avoid issues with charge separation during dissociation) and had been crystalized with Cu in the CSD. In total, 50 tridentate ligands were obtained from the hemilabile set, and 50 tridentate ligands were from the non-hemilabile set (25 small, 25 semisupervised). A complete list of the ligand refcodes and structures of the ligands are provided in the Supporting Information. We selected tridentate ligands for this test because tridentate ligands were the most challenging for our ML models to classify. The focus on copper is motivated by the fact that copper is the metal with the highest frequency in which we also have a high likelihood of finding a ligand in both high-and low-denticity binding configurations. We constructed complexes with the hemilabile or non-hemilabile ligand bound to a Cu complex that also contained chloride, in a four-coordinate tetrahedral or square planar geometry, depending on the ligand geometry. We computed partial dissociation energies of the three Cu-L bonds in these complexes. For these partial ligand dissociation energies (see Computational Methods) we observe that partial ligand dissociation is more favorable for hemilabile ligands, with a mean partial dissociation energy of -1.8 kcal/mol for the bidentate configuration relative to the tridentate bound conformation, whereas the partial dissociation energy of non-hemilabile ligands was found to be 2.3 kcal/mol, implying that the partial dissociation is more favorable for the hemilabile set (Supporting Information Figures ). Independent t-test analysis showed that these two sets have distinguishable population means, whereas the same test applied to the two non-hemilabile sets does not support a significant difference between the populations (Supporting Information Table ). Thus, our semi-supervised labeling strategy quantitatively distinguishes hemilabile and non-hemilabile ligands.