Notwithstanding the successful prediction accuracy of these ML models, practical applications are inappropriate since most of them required geometric information of relaxed structures, being only suitable for relaxed structure to relaxed energy (RS2RE) task . Thus, development of ML models for initial structure to relaxed energy (IS2RE) task is necessary to quickly predict properties of unrelaxed initial structures. Noh et al. reported a modified version of CGCNN (CGCNN-HD), where softplus activation function of the original CGCNN was replaced by hyperbolic tangent function and dropout algorithm was added for uncertainty quantification. This method achieved higher prediction accuracy (0.296 eV/atom) over CGCNN (0.518 eV/atom) for IS2RE task, and successfully discovered stable materials from elementsubstituted dataset without structural relaxations. Additionally, composition-based models have been developed to solve IS2RE task , e.g., Roost (Representation Learning from Stoichiometry) only takes the stoichiometry as input and does not use any geometric information. However, purely composition-based models are not capable of differentiating polymorphs. To overcome this limitation, Wren (Wyckoff Representation Regression) was developed by adding Wyckoff representations as the geometric input to the ML model , which are coordinate-free symmetry-related positions in a crystal, on top of the stoichiometric information.