Beyond molecule property prediction task, ML can tremendously impact chemical sciences by accelerating reaction network prediction . Building of reaction network requires very fast energy prediction as a large number of energy computation is necessary and thus DFT can be too computationally expensive for exploring large reaction networks. The use of cheap level DFT method can lower the computational cost but owing to the low fidelity of predicted energies could lead to erroneous analysis of the reaction network. Typically semi-empirical level theory, such as XTB are generally used but those have large errors as well.