The ab initio data set used for training the machine learning interatomic potential (MLIP) was generated by periodic DFT calculations using the QUICKSTEP/CP2K software. This code makes use of a hybrid basis set formed by Gaussian functions and plane waves, where the wave functions are described by the Gaussian basis with the addition of an auxiliary plane waves basis to describe the density. The Strongly Constrained and Appropriately Normed (SCAN) meta-GGA functional 7 was chosen for its good performance across a diverse range of chemical systems and properties of interest, in particular it reproduce satisfactorily the structural and elastic properties and the relative stability of different silica polymorphs . The inner core of Si and O were described by the Goedecker-Teter-Hutter (GTH) type of pseudopotential, with triple-ζ valence polarized Gaussian basis sets employed for the valence electrons.