To understand the effect of edge information, we evaluated prediction accuracies of D-CGCNN for IS2RE and RS2RE tasks with varying feature information describing connections between nodes: (1) bond distances (r), (2) bond angles (λ, φ) and ( ) both (r, λ, φ) (Figure and Table ). Note that bond distances tend to change significantly during the optimizations, while bond angles remain comparatively unchanged (Figure ). When only bond distances (bond angles) were used, the prediction accuracy for RS2RE was high (low) and RvIS2RE significant (negligible) (Figure ). When both angles and distances were used, the best accuracy for IS2RE was achieved and RvIS2RE was moderate. These results highlight that the combination of structure-sensitive and -insensitive features would be best suited to IS2RE task. ).