The XGBoost ML models trained to predict the hemilability of tridentate ligands on the randomly selected set (2,138 ligands total) show relatively poor performance, with an ROC-AUC of 0.72 and accuracy of 0.67, but this is improved by using the small set (ROC-AUC of 0.79 and accuracy of 0.73, 354 ligands total) and even more substantially by using a semi-supervised set (ROC-AUC of 0.94 and accuracy of 0.87, 2,138 ligands, Figure and Supporting Information Table ). For the tetradentate models, smaller dataset sizes (984 ligands for the randomly selected set) mean that we do not see the performance improvement from the randomly selected dataset (ROC-AUC of 0.81, accuracy 0.73, and recall 0.83) to the small dataset (ROC-AUC of 0.82, accuracy of 0.75, and recall 0.77, 222 ligands total), and we attribute this comparable performance to the small size (178 ligands) of the training set (Figure and Supporting Information Table ). Thus, the semisupervised approach is particularly critical in this case, giving by far the best model performance (ROC-AUC of 0.97, accuracy of 0.93, and recall 0.96). We also carried out the same grouped split test for tri-and tetradentate ligands, but we held out oxygen for tetradentate ligands due to both the limited number and class imbalance of phosphorus-coordinating ligands among the tetradentate set. These grouped split models using the semi-supervised labeled data still show good performance, with ROC-AUC of 0.88 and 0.92 for tri-and tetradentate ligands, respectively. To confirm the approach is not strongly sensitive to the ML model, we also trained support vector classifiers, random forest models, and multilayer perceptron models that all have comparable performance to the XGBoost model across all three ligand types (Supporting Information Tables ). Analysis of the coordination atom environment shows some differences and similarities between the two labeled sets in the total semi-supervised dataset (Figure ). As expected, hetero-donating ligands, where the identity of coordinating ligands differ, are more common among hemilabile ligand sets, which is a common design principle for hemilability. However, the non-hemilabile ligand set also features many hetero-donating ligands. Furthermore, homo-donating ligands are still frequent in the hemilabile ligand set. In particular, the hemilabile ligand set shows a higher number of bis-oxygen coordinating configurations, which can be attributed to the generally weaker donor ability of oxygen-coordinating ligands. While bis-nitrogen ligands are more common among non-hemilabile sets, they are still abundant within hemilabile sets. From the geometric analysis, we find that hemilabile ligands tend to have a slightly lower steric crowding near the metal based on the common steric descriptors such as buried volume or solvent-accessible surface area , which can be attributed to the decrease in ligand bite angle and weaker binding to the transition metal, based on the metal-ligand bond distances (Supporting Information Figures ). The overlap of the distributions of these simple descriptors highlights the difficulty of distinguishing hemilabile ligands from non-hemilabile ligands, necessitating more complex classification models. Analysis of the coordinating atom environment of tri-and tetradentate ligands shows that, similar to the bidentate ligands, the frequency of hetero-donating ligands is higher within the hemilabile set (Supporting Information Figures ). Unlike the bidentate set, we find that the all-nitrogen-donating ligand becomes the predominant class not only for the non-hemilabile ligand set but also for the hemilabile ligand set. Similarly, we see an increase in the total number of oxygen-donating ligands in the hemilabile class, which can be attributed to weaker donor strength of oxygen-donating ligands. Furthermore, there is a marked increase in the number of biscarbon donating ligands among the hemilabile set for both tri-and tetradentate ligands, which can be attributed to the π-coordinating alkene ligands, with a C=C bond forming a coordination geometry that our method assigns as bidentate. Similar to the bidentate ligand set, steric crowding around non-hemilabile ligands tends to be lower (Supporting Information Figures ). The consistent trends between ligand types demonstrate the internal consistency of our label assignment, however pervasive overlap between the two classes demonstrates the difficulty of identifying hemilabile ligands the need for classification models.