Graph Attention Neural Networks for Interpretable and Generalizable Prediction of Janus III–Vi Van Der Waals Heterostructures

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Abstract
Graph neural networks (GNNs) have been widely used in materials science due to their ability to process complex graph‐structured data by capturing the relationships between atoms, molecules, or crystal structures within materials. However, due to the lack of interpretability, GNN acts as a “black box” model in most cases. In this work, by introducing the attention mechanism into GNN, a graph attention neural network (GANN) model for 2D materials is proposed, which not only reaches accurate prediction performances but also shows interpretability via attention mechanism analysis. Taking Janus III–VI van der Waals heterostructures as a representative case, the MAE for predicting formation energy, lattice constants, PBE bandgap, and HSE06 bandgap are 0.009 eV, 0.003 Å, 0.087, and 0.123 eV, respectively. Remarkably, the GANN model shows outstanding generalizable ability that can achieve accurate prediction using guessed input structures without fully structural relaxation to reach the ground state for Janus III–VI vdW heterostructures. Furthermore, the attention mechanisms of integration enable qualitative analysis of the contributions of the nearest neighboring atoms, endowing the GNN model with enhanced interpretability. Our findings offer novel perspectives for AI‐driven 2D material design by establishing an optimal balance between predictive accuracy and model interpretability in GNN approaches.
Funding Information
  • National Natural Science Foundation of China (52201022)
  • Natural Science Foundation of Fujian Province (2024J01262)

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