In addition to this generic featurization strategy, we also include domain specific features by supplementing p with ∆H b , each element's binary hydride formation enthalpies, which we obtained from computed entries in Materials Project 52 since this value may not be experimentally accessible for non-hydriding elements. Gradient boosting tree regressors and classifiers were found to be a well performing modeling approach for predicting hydride thermodynamics and HEA phase stability and implemented using the scikit-learn library. Hyperparameters were tuned to minimize over-fitting and K-fold cross validation test set errors (as provided in the SI of our previous work). The jupyter notebooks and data needed to reproduce this study are made publicly available ().