The expanded molecular graph can then be input into a GNN. GNNs are a type of ANN able to handle variable-size non-euclidean data, represented as graphs. To accomplish this, our GNN model is built by assembling three against the E {i,DFT} values of the test sets of 0.17 eV. Considering that the consensus error of DFT in adsorption systems is about 0.20 eV and that our values can be both above or below the 1:1 line, we conclude that the error of the method is similar to that of DFT itself. However, once trained with a sufficiently large and diverse dataset as that in FG-dataset, the true advantage is the fast estimation of the DFT energy, that takes place in the order of seconds in a single CPU.