The obtained GNN model generalizes the chemical patterns found within the FG-dataset, which presents functional groups that are the commonly found as building blocks of more complex chemical structures. In this context, we tested the GNN model, trained with the FG-dataset, on industrially relevant larger molecules that are not amenable to DFT. Before testing the GNN model with new samples, we performed an hyperparameter optimization study, see Note S8 and Table . This resulted into a simpler model Overall, the GNN demonstrates its robustness when explaining the chemical process of adsorption. Since functional groups in the dataset present a wide variety, the model generalizes to bigger compounds, paving the way for a fast tool to evaluate adsorption. The FG-dataset generation requires 10 5 s but these are small calculations on relatively small unit cells, thus they can be run in modest computational facilities. The large molecules of industrial interest still would require massive computational resources due to the large unit cells required and sequential DFT evaluations in powerful machines.