  Machine learning potentials (MLPs) are widely applied as an efficient
alternative way to represent potential energy surfaces (PES) in many chemical
simulations. The MLPs are often evaluated with the root-mean-square errors on
the test set drawn from the same distribution as the training data. Here, we
systematically investigate the relationship between such test errors and the
simulation accuracy with MLPs on an example of a full-dimensional, global PES
for the glycine amino acid. Our results show that the errors in the test set do
not unambiguously reflect the MLP performance in different simulation tasks
such as relative conformer energies, barriers, vibrational levels, and
zero-point vibrational energies. We also offer an easily accessible solution
for improving the MLP quality in a simulation-oriented manner, yielding the
most precise relative conformer energies and barriers. This solution also
passed the stringent test by the diffusion Monte Carlo simulations.
