The relative importance of each feature for the prediction of binding energies was then extracted from the random forest model. The Bader charge of adsorbate oxygen was the most important feature of all of them, with the O2p bandcenter of the adsorbate oxygen atom coming at a distant second(Figure ). The significance of electronic structure features associated with adsorbate oxygen for OER is corroborated by previous work by Nørskov and coworkers, which demonstrated a link between the O2p bandcenter of adsorbate oxygen and the calculated theoretical overpotential . We also decided to use LASSO to predict the binding energies of the OER intermediates, in order to determine how universal the features we identified were with respect to their relationship with the binding energies of OER (Figure ). Just like with the random-forest model, the Bader charge of adsorbed oxygen was determined to be the most important feature. The second most important feature in the LASSO model, however, was deemed to be the Bader charge of surface oxygen. The LASSO model performed poorly at predicting binding energies compared to the random-forest model; with an R 2 value of 0.65 for the training set, an R 2 value of 0.72 for the test set and a median absolute error of 0.56eV for both the training and the test sets. As such, we decided to use the results of the random-forest model for further analysis instead.