Using this dataset of hemilabile ligands, we next devised a strategy to train machine learning (ML) models that could predict the likelihood of a ligand to exhibit hemilability for bidentate ligands. To train such a model, we require not just the hemilabile ligand dataset but also a set of non-hemilabile ligands to serve as the negative class. Although positive examples of hemilability are identifiable based on the presence of complexes with ligands in multiple denticities, the absence of multiple denticities for ligands across complexes could be due to a lack of diversity of synthesized complexes containing a given ligand rather than to a lack of hemilabile ligand character. To address this issue, we defined three different non-hemilabile sets. First, we randomly subsampled all unlabeled bidentate ligands (i.e., those not positively identified as hemilabile in the preceding analysis) to obtain an equal number of probable non-hemilabile ligands as labeled hemilabile ligands (random dataset, Figure ). Next, we used a small set of ligands with an expected lack of hemilability supported by frequent and diverse appearance in the CSD with a single denticity (small dataset, Figure and see Supporting Information Text S2). Finally, we used a semi-supervised learning strategy that augments the small non-hemilabile dataset by using machine learning to identify more non-hemilabile ligands (semi-supervised dataset, Figure and Supporting Information Text S2). Semi-supervised learning encompasses a broad set of techniques that combine aspects of supervised (i.e., with labeled data) and unsupervised (i.e., with unlabeled data) learning approaches to address the challenges of partially labeled datasets. This approach enables construction of a model that can benefit from the large size of the knownhemilabile set while preserving good labels for non-hemilabile ligands. We used these datasets to train classification models using the extreme gradient boosting algorithm (XGBoost) for the prediction of hemilability (Figure ). To featurize ligands, we used ligand-based revised autocorrelations (RACs) , which are connectivity-based representations that have been successfully applied to transition metal complex property prediction (see Computational Details). The model trained on the randomly subsampled dataset shows promising performance on a set-aside test set with good separation between two classes with a receiver-operating characteristic area under the curve (ROC-AUC) of 0.86 as well as good accuracy (0.80) and recall (0.80), despite our expectation of potential label contamination due to incorrectly assigned negative labels (Figure and Supporting Information Figures S9-S10 and Table ). Despite a significantly diminished training set size for the small dataset (i.e., an 80% training partition of 3,062 ligands versus of 588 ligands in the small set), the model trained on this dataset shows a slight improvement over the randomly sampled set in terms of its predictive power on the test set, including an improved ROC-AUC of 0.88, accuracy of 0.81, and a comparable recall of 0.80 (Figure and Supporting Information Figures S11-S12 and Table ). Finally, we trained an ML model classifier (i.e., again XGBoost) using the full set of identified hemilabile ligands and a sizematched negative class made up of the small set of non-hemilabile ligands augmented by label spreading. While the majority of the negative, non-hemilabile class was defined using the semisupervised augmentation technique, the set of hemilabile ligands remained unchanged. Therefore, to ensure that the hemilabile ligands in the test set were unseen during the augmentation approach, label spreading approach was implemented using only the hemilabile ligands in the training set (i.e., 80%), and the set-aside hemilabile ligands were used to construct the test set for the final model. This model shows by far the best overall performance, with a marked improvement that includes an ROC-AUC of 0.96, accuracy of 0.90, and recall of 0.89 (Supporting Information Figures S13-S14 and Table ).