  Machine-learning force fields have been increasingly employed in order to
extend the possibility of current first-principles calculations. However, the
transferability of the obtained potential can not always be guaranteed in
situations that are outside the original database. To study such limitation, we
examined the very difficult case of the interactions in gold-iron
nanoparticles. For the machine-learning potential, we employed a linearized
formulation that is parameterized using a penalizing regression scheme which
allows us to control the complexity of the obtained potential. We showed that
while having a more complex potential allows for a better agreement with the
training database, it can also lead to overfitting issues and a lower accuracy
in untrained systems.
