Where 𝜀 refers to the bandcenter, 𝜀 refers to the energy level, 𝜌 refers to the density of states at that energy level, 𝜀 𝑚𝑎𝑥 refers to the maximum energy level within this range of integration and 𝜀 𝑚𝑖𝑛 refers to the minimum energy level within this range of integration. The Bader charge was calculated using a Bader charge analysis code provided by the Henkelman group . The average bandcenters and Bader charges of all atoms were used to train the random forest model. To train and test the models, the dataset was split using train_test_split from scikitlearn, saving 20% as test set. The optimal model hyperparameters were identified using a Bayesian optimization approach (BayesSearchCV from scikit-optimize, on RandomForestRegressor from scikit-learn) on the training set with a 5-fold cross validation. The hyperparameters of the model that were optimized were the number of trees in the random forest model, the maximum depth of each tree, the minimum number of samples required to split an internal node, the minimum number of samples required to be at a leaf node and the number of features to consider when looking for the best split. For comparison, LASSO was also tested to predict the binding energies of the OER intermediates (Figure ). The same features used to train the random-forest model were used to train the LASSO model as well, and the hyperparameters were also optimized using Bayesian optimization. The dataset for the LASSO model was also split similarly to the random-forest model, with 20% of the dataset being saved for the test set. The hyperparameter tuned for the LASSO model is the alpha parameter that determines the magnitude of the L1 regularization term.