Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks.

ABSTRACT: Modeling in heterogeneous catalysis requires the extensive evaluation of the energy of molecules adsorbed on surfaces. This is done via density functional theory but for large organic molecules it requires enormous computational time, compromising the viability of the approach. Here we present GAME-Net, a graph neural network to quickly evaluate the adsorption energy. GAME-Net is trained on a well-balanced chemically diverse dataset with C_1-4 molecules with functional groups including N, O, S and C_6-10 aromatic rings. The model yields a mean absolute error of 0.18 eV on the test set and is 6 orders of magnitude faster than density functional theory. Applied to biomass and plastics (up to 30 heteroatoms), adsorption energies are predicted with a mean absolute error of

SUBMITTER: Pablo-Garcia S 

PROVIDER: S-EPMC10766545 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature