Starting with the dataset of intermetallic Materials Project structures created from bulks with nonpositive formation energies at most 0.1 eV above hull enumerated in the Open Catalyst Dataset , slabs were enumerated using Open Catalyst Dataset enumeration tools, and CatKit was used to position adsorption sites over the surfaces . All bare structures larger than 80 atoms and all slabs larger than 100 atoms were discarded due to computational constraints, representing less than 1% of structures and approximately 30% of slabs. Hydrogen adsorption energies of the adsorbate-slab systems were then calculated using GemNet, a graph neural network that uses message passing with directed edge embeddings to predict materials properties from structures . The model had been trained on the Open Catalyst Dataset and is available on the Open Catalyst Project website . Systems were relaxed for 90 steps, a procedure which accurately predicts validation data (see Figure ). After adsorption energies were predicted, the minimum (strongest) adsorption energy was taken to represent the surface.