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Figure 3. (A) The model architecture of AGAT. The top panel denotes the AGAT layer, and the bottom panel denotes the AGAT model; (B) The interpretability of the AGAT model. The attention scores of the energy and forces models compared with the energy and forces variations; (C) ML-predicted vs. DFT-calculated adsorption enthalpies in 5-fold cross-validation using RBF-GPR, WWL-GPR, and XGBoost for the simple adsorbates database, respectively; (D) The model architecture of WWL-GPR. The adsorption enthalpy for the relaxed structure is predicted by representing the initial structure as a graph. Node attributes are calculated based on the gas-phase molecule and the pristine surface. The similarity between graphs is assessed using the WWL graph kernel, and this information is then used in a GPR model. Reproduced with permission from refs^[78,79]. Copyright 2023 Elsevier and Copyright 2022 Springer Nature, respectively. AGAT: Atomic graph attention; ML: machine learning; DFT: density-functional theory; RBF-GPR: radial basis function and Gaussian progress regression; WWL-GPR: Wasserstein Weisfeiler-Lehman graph kernel and GPR.