Fast Evaluation of the Adsorption Energy of Organic Molecules on Metals via Graph Neural Networks

Modeling of solid-state material-molecule interfaces in heterogeneous catalysis requires the extensive evaluation of the energy of molecules on surfaces. Obtaining the binding energy of many configurations of large organic molecules requires a vast amount of computational time with density functional theory (DFT). Here, we use a graph neural network (GNN) to evaluate the adsorption energy of molecular species adsorbed on metallic surfaces. The GNN is trained on a set of C1–4 fragments including N, O, S heteroatoms and C6–10 aromatic rings. Compared to DFT, the GNN shows a mean absolute error (MAE) of 0.17 eV on the test set being 6 orders of magnitude faster. When applying the trained model with subsequent hyperparameter optimization to molecules of industrial interest (biomass, plastics and polyurethanes precursors) containing up to 22 carbon atoms, the prediction performance for the adsorption energy yields a MAE of 0.03 eV/(non-H atom). While the error for out-of-distribution molecules is higher, it is still within the acceptable limit for adsorption energies (0.05 eV/atom), confirming the viability of the approach. The proposed framework represents a potential tool for the fast screening of catalytic materials, as well as their inverse design, enabling the multi-scale modeling for systems that cannot be easily simulated by DFT.