All training uses ALIGNN model architecture . In ALIGNN, a crystal structure is represented as a graph by mapping atoms at the lattice sites to nodes and bonds to the edges of the graph. Each node in the crystal graph is assigned 10 input node features based on its atomic species: electronegativity, group number, period number, covalent radius, number of s, p, and d valence electrons, first ionization energy, electron affinity, and the number of unpaired electrons. The inter-atomic bond distances are used as edge features with radial basis function up to 8 Å cut-off and 12 nearest-neighbors. This crystal graph is then used for constructing the corresponding line graph that encodes interatomic bond-distances as nodes and bond-angles as edge features. The ALIGNN model learns the relationship between a graph and the corresponding target property , i .e., the adsorption energy of a specific adsorbate on a fully relaxed surface or the potential energy of a catalyst system. To evaluate generalizability, the ALIGNN model was trained with a specific dataset first and the pre-trained model was used to predict adsorption energies of unknown systems in other datasets.