  Studying the crystallization process of silicon is a challenging task since
empirical potentials are not able to reproduce well the properties of both
semiconducting solid and metallic liquid. On the other hand, nucleation is a
rare event that occurs in much longer timescales than those achievable by
ab-initio molecular dynamics. To address this problem, we train a deep neural
network potential based on a set of data generated by Metadynamics simulations
using a classical potential. We show how this is an effective way to collect
all the relevant data for the process of interest. In order to drive
efficiently the crystallization process, we introduce a new collective variable
based on the Debye structure factor. We are able to encode the long-range order
information in a local variable which is better suited to describe the
nucleation dynamics. The reference energies are then calculated using the SCAN
exchange-correlation functional, which is able to get a better description of
the bonding complexity of the Si phase diagram. Finally, we recover the free
energy surface with a DFT accuracy, and we compute the thermodynamics
properties near the melting point, obtaining a good agreement with experimental
data. In addition, we study the early stages of the crystallization process,
unveiling features of the nucleation mechanism.
