  Looking back at seven decades of highly extensive application in the
semiconductor industry, silicon and its native oxide SiO$_2$ are still at the
heart of several technological developments. Recently, the fabrication of
ultra-thin oxide layers has become essential for keeping up with trends in
down-scaling of nanoelectronic devices and for the realization of novel device
technologies. With this comes a need for better understanding of the atomic
configuration at the Si/SiO$_2$ interface. Classical force fields offer
flexible application and relatively low computational costs, however, suffer
from limited accuracy. Ab-initio methods give much better results but are
extremely costly. Machine learning force fields (MLFF) offer the possibility to
combine the benefits of both worlds. We train a MLFF for the simulation of the
dry thermal oxidation process of a Si substrate. The training data is generated
by density functional theory calculations. The obtained structures are in line
with ab-initio simulations as well as with experimental observations. Compared
to a classical force field, the most recent reactive force field (reaxFF), the
resulting configurations are vastly improved.
