One of the strategies in maintaining the balance between the QM accuracy and computational cost is to teach multidimensional PES of the system at the QM accuracy by machine learning (ML) techniques. To date, there have been numerous neural network potentials (NNPs) to represent the multidimensional PES of the systems at the QM accuracy. In principle, if the PES of the system is well represented by NNPs, the energy minimization and conformer search can be made by using ML potentials rather than costly QM calculations or less accurate MM calculations.