Now that we have demonstrated that the success of this computational workflow at discovering new catalysts, we have decided to use the database created by this workflow to train a machine learning model to predict the binding energies of *O, *OH and *OOH intermediates. The objective of this task is to use this model to identify patterns, based on DFT-calculated electronic structure descriptors, that could aid with the swift screening of OER catalysts. Electronic structure descriptors have been successfully used in previous studies as a computationally inexpensive means of predicting OER activity . The bulk 3d band center in metallic catalysts and the bulk O2p bandcenter in perovskites have been shown to be predictive of OER activity in previous studies . However, the main weakness of solely using bulk descriptors is the likelihood that surface effects end up getting ignored. Stoerzinger and coworkers demonstrate this reality when they showed that OER activity on the surface of RuO2(100) and RuO2(101) was higher than their (110) and (111) counterparts in basic conditions due to the higher concentration of coordinatively unsaturated sites . Any comprehensive assessment of the link between OER activity and electronic structure descriptors would have to include electronic structure descriptors associated with both bulk and surface atoms as well. Inspired by a recent study by Lunger and coworkers which concluded that the Bader charge and the O2p bandcenter of surface oxygen was predictive of *O, *OH and *OOH binding energies on the surface of perovskite slabs, we decided to use the bandcenters and Bader charges of the atoms in each slab as features for our model . We first calculated the O2p bandcenters of the surface oxygen, bulk oxygen and adsorbate oxygen atoms, the M3d bandcenters of the surface metal and bulk metal atoms and the Bader charges of the surface oxygen, bulk oxygen, adsorbate oxygen, surface metal and bulk metal atoms. These descriptors were then used as training data for a random-forest regression model. We used a random-forest model since they are known to be simple to use, accurate, and capable of dealing with small sample sizes and high-dimensional feature spaces . In addition, since random forests are also interpretable, we can also look at the relative importance of the input features to find electronic structure descriptors that can correlate well with predicted OER activity . This model was able to predict the binding energies of *O, *OH and *OOH intermediates quite accurately, with an R 2 score of 0.83 for the training set and 0.78 for the test set (Figure ). We also found that the outliers of our model, defined as a prediction which differs from the calculated value by more than 1eV, were far more likely to contain novel elements like Nb, Pb, Cd, and Ir. The failure of our model to predict the theoretical overpotentials of these structures can be attributed to the fact that these materials were underrepresented in our dataset, since spinel oxides fabricated using these elements were far more likely to be unstable and were therefore filtered out by the convex hull stability screen. Such outliers, thankfully, are rare and can only be attributed to 4% of the calculations in our screen, further demonstrating the efficacy of our model at predicting OER binding energies.