CGCNN-HD showed the highest (lowest) prediction accuracy for RS2RE (IS2RE) task. This is because CGCNN-HD used interatomic distance-based input representations, where the representations of the initial and relaxed structures could be significantly different due to structural changes during geometry optimizations (Figure and Figure ). The target value, Ef, is calculated using the ground state energy of relaxed structures , which is determined from a potential energy surface (PES) in terms of multiple variables, i.e., lattice parameters, cell shapes and three dimensional positions of each atom in crystal structures. Since IS is far from the ground state of the PES, the model trained using RS and the corresponding ground state energy is incapable of successfully predicting the ground state energy of IS.