All sections detect and allocate FNN atom similarly unless there is no atom in that direction within the cutoff distance. As a result, no more atoms than the number of sections (S) was captured, and nodes of the captured FNN atoms (ni,j) were connected to the node of the center atom (ci) in the graph (Figure ). The bond information between center and neighboring atoms was encoded as a tuple of one spatial distance (r) and two angles (λ, φ) expanded by Gaussian basis. During the DFT optimization processes, both cartesian coordinates of atoms and distances between neighboring atoms could change significantly, resulting in different graph representations for IS and RS. On the other hand, direction and the FNN atom do not change significantly, thus the graph representations for both IS and RS are expected to be similar, making it suitable for IS2RE task . The similarity of graph representations between IS and RS will be compared with CGCNN, Roost and Wren (Figures ) in a later section to analyze the effect of graph similarity on the prediction performance of IS2RE task. We note that our representation is rotationally variant in concept since a choice of the initial axis is determined by two arbitrary atoms. To understand the effect of rotation on the prediction accuracy, we compared test set MAE with different degrees of rotation (0, 0.25θ, 0.50θ, 0.75θ, θ). The difference was found to be marginal, suggesting negligible effects of the rotation on the model accuracy (Figure ).