Molecular dynamics (MD) simulations are one of the most effective computational methods for the direct observation of dynamic phenomena. For example, MD simulations with interatomic potentials have contributed to the elucidation of phenomena such as superionic conduction mechanisms , carbon nanotube formation , and protein folding . The recent developments of machine-learning potentials have accelerated and facilitated the potential construction of new materials. These developments have enabled MD simulations to be applied to a wider variety of materials . In fact, machinelearning potential (MLP) MD simulations have accurately reproduced the phase transition behavior of high-pressure H2, which is strongly affected by the simulation cell size . In addition, machine learning potentials are frequently used to explore the bulk properties of hydrides and the structure of ternary systems .