fig4

Figure 4. GNN model for CO Adsorption Prediction. (A) The graph data extraction method for CO adsorption configurations, detailing the steps from detecting neighboring atoms under van der Waals conditions to merging local subgraphs into a feature graph that accurately represents the adsorption environment of CO molecules on Cu surfaces. (D) The comparison of the GNN model’s predictive performance between using only the DFT calculation dataset corresponding to (B) and using the DFT + MLFF calculation dataset corresponding to (C). (E) The computational efficiency gains of the DFT + MLFF + GNN workflow compared to traditional DFT and DFT + MLFF methods, showcasing a significant reduction in computational cost and time, thus enabling the study of vast adsorption configuration spaces with enhanced efficiency. DFT: Density functional theory; MLFF: machine-learning force field; GNN: graph neural network; RMSE: root mean square error; GEN: graph embedding network model; MAE: mean absolute error; MAPE: mean absolute percentage error.