  GeTe is a prototypical phase change material of high interest for
applications in optical and electronic non-volatile memories. We present an
interatomic potential for the bulk phases of GeTe, which is created using a
neural network (NN) representation of the potential-energy surface obtained
from reference calculations based on density functional theory. It is
demonstrated that the NN potential provides a close to ab initio quality
description of a number of properties of liquid, crystalline and amorphous
GeTe. The availability of a reliable classical potential allows addressing a
number of issues of interest for the technological applications of phase change
materials, which are presently beyond the capability of first principles
molecular dynamics simulations.
