A neural network interatomic potential for the phase change material GeTe

TL;DR

This paper introduces a neural network-based interatomic potential for GeTe, accurately modeling its various phases and enabling large-scale simulations beyond traditional ab initio methods.

Contribution

A novel neural network potential for GeTe that replicates ab initio accuracy, facilitating advanced simulations of phase change material properties.

Findings

Close to ab initio quality in modeling GeTe phases

Enables large-scale molecular dynamics simulations

Accurately describes liquid, crystalline, and amorphous GeTe

Abstract

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.