  In this paper we test two strategies to improving the accuracy of
machine-learning potentials, namely adding more fitting parameters thus making
use of large volumes of available quantum-mechanical data, and adding a
charge-equilibration model to account for ionic nature of the SiO2 bonding. To
that end, we compare Moment Tensor Potentials (MTPs) and MTPs combined with the
charge-equilibration (QEq) model (MTP+QEq) fitted to a density functional
theory dataset of alpha-quartz SiO2-based structures. In order to make a
meaningful comparison, in addition to the accuracy, we assess the uncertainty
of predictions of each potential. It is shown that adding the QEq model to MTP
does not make any improvement over the MTP potential alone, while adding more
parameters does improve the accuracy and uncertainty of its predictions.
