Neural network interatomic potential for the phase change material GeTe

Abstract

GeTe is a prototypical phase change material of high interest for applications in optical and electronic nonvolatile 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.