The graph compresses the information of the atoms and the connectivity in a simple data structure, in analog way to Benson's approach. Graph neural networks (GNN), which are ANNs for the graph data structure type, have been successfully applied to predict chemical properties of molecules and materials. For gas-phase molecules, GNNs have been able to predict molecular properties and their solvation energy with exceptional performance. Extending to materials chemistry, specific convolutional graph layers can describe periodicity and predict the structure of metals and crystals. For the adsorption of molecules on metals, GNNs have been employed to estimate the DFT binding energy of small species as CO 2 , O, in the open catalyst dataset (OC20 and OC22). Moreover, GNNs have been employed to assess lat-eral adsorbate-adsorbate interactions. Taking eight C 1 and C 2 fragments (C, H, O) on metals with different adsorbate-surface connectivity (1422 data points), Xu et al. coupled a graph kernel to a gaussian process regressor (GPR) and obtained a reasonable performance for small molecules (up to 9