fig8

Figure 8. (A) The architecture of GNNs for classification and regression tasks; (B) The normalized confusion matrix for test data. Each row corresponds to different true classes, and each column corresponds to predicted classes; the diagonal represents the percentage of correct predictions for each class; (C) Prediction diagram of strain response for single-molecule NH₃ synthesis on Cu₄S₂ (110) surface by regressor; (D and E) Verification and comparison of strain phase diagrams for HfCu₃(100) surface adsorption with ^*N and ^*NO₂ using DFT. Copyright 2022, The American Association for the Advancement of Science, Reproduced with permission^[119]; (F) Diagram of the GNN architecture applied in this study; (G) The workflow diagram of combining ML with DFT screening; (H) The pairing diagram of CGCNN model, DFT-calculated $$ \Delta G_{H^{\ast}} $$, and ML-predicted $$ \Delta G_{H^{\ast}} $$. Copyright 2023, American Chemical Society, Reproduced with permission^[120]. GNNs: Graph neural networks; DFT: density functional theory; ML: machine learning; CGCNN: crystal graph convolutional neural network.