To solve the problem with the data, Ulissi et al. developed the OC20 dataset, consisting of 1,281,040 Density Functional Theory (DFT) relaxations (∼264,890,000 single point evaluations) across a wide swath of materials, surfaces, and adsorbates (nitrogen, carbon, and oxygen chemistries). Compared to previously published catalyst datasets the OC20 dataset allows us to generalize across different intermediates, forming during CO2 and N2 reduction reactions. In addition to generating and sharing the dataset, authors propose three related domain challenges as an open competition: (1) predict the energy and force for a given state (S2EF), (2) predict a nearby relaxed state given an initial starting state (IS2RS), and (3) predict the relaxed adsorption energy given an initial state (IS2RE). The dataset is split into train validation and test splits indicative of common situations in catalysis: predicting these properties for a previously unseen adsorbate, for a previously unseen crystal structure or composition, or both. For a practical purpose, the most suitable task is a prediction of DFT-relaxed binding energies based on heuristically generated initials catalyst-adsorbate structures. This allows directly evaluating overpotential, required for electron transfer from the surface to an adsorbate during the rate-determining step. Adsorption energy (Ea) can be defined as the decreasing energy while two materials are combined under the adsorption process in which an atom, ion, or molecule (adsorbate) is attached to the surface of a solid (adsorbent). Ea can be obtained as follows, by calculating the difference between the energy of the adsorption model and the sum of the energy which is separately calculated for each structure composing the adsorption structure.