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

06 October 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


Graph Neural Networks
Heterogeneous Catalysis
Adsorption Energy
Density Functional Theory
Molecular Graphs

Supplementary materials

Supplementary Information
The Supplementary Information contains the technical details about the applied methodology for developing the Graph Neural Network framework and the datasets creation process (FG- and BM-dataset).

Supplementary weblinks


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