A graph neural network for predicting the adsorption energy of molecules on metal surfaces

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.