Machine learning is rapidly transforming many research areas including computational catalysis. Today, machine learning models trained on historical databases of computations can calculate adsorption energies 3 orders of magnitude faster than the equivalent density functional theory procedure while retaining reasonable accuracy . The existence of ML models therefore creates many new opportunities to expand the scope of computational screening in catalyst However, a major limitation to this vision today is determining the activation energies of the reactions; data points on activation energies are limited to a few thousand at best. It is possible that more universal ML models, trained further out of equilibrium and near transition state structures using expanded data sets, will help remove this limitation in the future.