A graph neural network — GAME-Net — has been developed to predict the adsorption energy of organic molecules on metal surfaces, which is a key descriptor of heterogeneous catalytic activity. This method allows for the study of large molecules derived from raw materials such as plastic waste, avoiding the use of costly and time-intensive first-principles simulations.
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This is a summary of: Pablo-García, S. et al. Fast evaluation of the adsorption energy of organic molecules on metals via graph neural networks. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00437-y (2023).
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A graph neural network for predicting the adsorption energy of molecules on metal surfaces. Nat Comput Sci 3, 372–373 (2023). https://doi.org/10.1038/s43588-023-00449-8
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DOI: https://doi.org/10.1038/s43588-023-00449-8
