Neural network potentials (NNP) have been proposed as a compromise between CMD and DFT-MD and have been applied to a wide range of materials. Highly expressive NNP can be used to estimate the potential energy surface (PES) derived from DFT-MD. Therefore, neural network molecular dynamics (NNMD) simulation using NNP can achieve the same level of accuracy as DFT-MD. NNMD is also suitable for performing large-scale calculations equivalent to those possible with CMD, enabling simulation of systems with thousands to tens of thousands of atoms for nano-second periods. In recent years, NNMD has been applied to silica,