D-CGCNN demonstrated moderate prediction accuracy for RS2RE, i.e., Wren/Roost < D-CGCNN < CGCNN-HD, but the best performance for IS2RE of 2e-ORR dataset, indicating that direction-based representations of IS and FS do not differ as much as distance-based ones in CGCNN (Figure ). For WBM dataset, the trend remained identical to that of 2e-ORR dataset except for Wren, of which MAE for IS2RE was lower than that of D-CGCNN, while RMSE was higher (Figure and Table ). This is because RMSE is more sensitive to outliers than MAE, where we found many cases that Wren mispredicted significantly (> 3 eV/atom) near 0 eV/atom of E f DFT (Figure ). Considering that 0 eV/atom of Ef is the criterion to determine the stability of materials, this result suggests that D-CGCNN is more advantageous in stability classification compared to Wren.