  A machine-learned interatomic potential for Ge-rich Ge$_x$Te alloys has been
developed aiming at uncovering the kinetics of phase separation and
crystallization in these materials. The results are of interest for the
operation of embedded phase change memories which exploits Ge-enrichment of
GeSbTe alloys to raise the crystallization temperature. The potential is
generated by fitting a large database of energies and forces computed within
Density Functional Theory with the neural network scheme implemented in the
DeePMD-kit package. The potential is highly accurate and suitable to describe
the structural and dynamical properties of the liquid, amorphous and
crystalline phases of the wide range of compositions from pure Ge and
stoichiometric GeTe to the Ge-rich Ge$_2$Te alloy. Large scale molecular
dynamics simulations revealed a crystallization mechanism which depends on
temperature. At 600 K, segregation of most of Ge in excess occurs on the ns
time scale followed by crystallization of nearly stoichiometric GeTe regions.
At 500 K, nucleation of crystalline GeTe occurs before phase separation,
followed by a slow crystal growth due to the concurrent expulsion of Ge in
excess.
