  Si and its oxides have been extensively explored in theoretical research due
to their technological and industrial importance. Simultaneously describing
interatomic interactions within both Si and SiO$_2$ without the use of
\textit{ab initio} methods is considered challenging, given the charge
transfers involved. Herein, this challenge is overcome by developing a unified
machine learning interatomic potentials describing the Si/ SiO$_2$/ O system,
based on the moment tensor potential (MTP) framework. This MTP is trained using
a comprehensive database generated using density functional theory simulations,
encompassing a wide range of crystal structures, point defects, extended
defects, and disordered structure. Extensive testing of the MTP is performed,
indicating it can describe static and dynamic features of very diverse Si, O,
and SiO$_2$ atomic structures with a degree of fidelity approaching that of DFT
