The high cost of direct MD simulations motivated the further study of whether stability could be predicted based solely on ligand features. The 11k perovskites characterized by MD simulations were used as training data for machine learning models capable of classifying unseen ligand candidates as stable or unstable. Each ligand was featurized based on nine inexpensive graph or structure-based properties (See Methods in SI for feature choice and definition). Critically, none of these features depend on MD simulations. Other featurizations may be desirable, but the high performance of the resulting models was sufficient for downselecting ligands for synthesis (vida inf ra). Supervised models were trained using these features and several popular architectures, including Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Classification (SVC), Gaussian Naïve Bayes (GNB), and Random Forest (RF) to compare performance. The OE series has an 88.9% overall stabilization rate, thus synthetic minority oversampling (SMOTE) was used to balance the dataset. The balanced dataset was used for training and testing using an 80:20 split (See Methods in SI).