  Molecular dynamics simulations have been used in different scientific fields
to investigate a broad range of physical systems. However, the accuracy of
calculation is based on the model considered to describe the atomic
interactions. In particular, ab initio molecular dynamics (AIMD) has the
accuracy of density functional theory (DFT), and thus is limited to small
systems and relatively short simulation time. In this scenario, Neural Network
Force Fields (NNFF) have an important role, since it provides a way to
circumvent these caveats. In this work we investigate NNFF designed at the
level of DFT to describe liquid water, focusing on the size and quality of the
training data-set considered. We show that structural properties are less
dependent on the size of the training data-set compared to dynamical ones (such
as the diffusion coefficient), and a good sampling (selecting data reference
for training process) can lead to a small sample with good precision.
