In summary, we developed a data-driven workflow for identifying hemilabile to accelerate catalyst screening. We used a semi-supervised learning approach to leverage a combination of labeled and unlabeled data to confidently identify examples of non-hemilabile ligands. We trained ML models that can predict ligand hemilability for bidentate and tetradentate ligands with high accuracy, and we also demonstrated a model for tridentate ligands with a suggested strategy for further validation using DFT. We showed that coordinating atom identity alone fails to account for ligand hemilability. Feature importance analysis of machine learning models highlights why conventional design principles can be insufficient for the identification of hemilabile ligands, due to the high significance of metal-distant and structural features. We used trained machine learning models to identify a large number of ligands that are predicted to be hemilabile and that can be used for accelerated discovery of new catalytic reactions. updates. A dummy atom with identical connectivity to the metal with an atomic number of 0 was introduced to identify ligands without preserving metal identity. For each ligand with a dummy atom, the atomic number and bond-order weighted connectivity matrix determinant were calculated to identify unique ligands, including their metal-ligand connectivity, as described in ref . Atomic number and bond-order weighted connectivity matrix determinant in the absence of a dummy atom was also calculated to identify the same ligands with differing transition metal connectivity.