Unraveling the Crystallization Kinetics of the Ge${}_2$Sb${}_2$Te${}_5$ Phase Change Compound with a Machine-Learned Interatomic Potential

Abstract

The phase change compound Ge${}_2$Sb${}_2$Te${}_5$ (GST225) is exploited in advanced non-volatile electronic memories and in neuromorphic devices which both rely on a fast and reversible transition between the crystalline and amorphous phases induced by Joule heating. The crystallization kinetics of GST225 is a key functional feature for the operation of these devices. We report here on the development of a machine-learned interatomic potential for GST225 that allowed us to perform large scale molecular dynamics simulations (over 10000 atoms for over 100 ns) to uncover the details of the crystallization kinetics in a wide range of temperatures of interest for the programming of the devices. The potential is obtained by fitting with a deep neural network (NN) scheme a large quantum-mechanical database generated within Density Functional Theory. The availability of a highly efficient and yet highly accurate NN potential opens the possibility to simulate phase change materials at the length and time scales of the real devices.

References in corpus (14)

• Separable Dual Space Gaussian Pseudo-potentials
• Escaping free-energy minima
• Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
• DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
• Supercooled Liquids for Pedestrians
• Coexistence of tetrahedral and octahedral-like sites in amorphous phase change materials
• A neural network interatomic potential for the phase change material GeTe
• 86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
• Silicon liquid structure and crystal nucleation from ab-initio deep Metadynamics
• Molecular Dynamics Computer Simulation of Crystal Growth and Melting in Al50Ni50
• On the Study of Collective Dynamics in Supercooled Liquids through the Statistics of the Iso-Configurational Ensemble
• Breakdown of Stokes-Einstein relation in the supercooled liquid state of phase change materials
• Uncovering the effects of interface-induced ordering of liquid on crystal growth using machine learning
• A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment

Cited by in corpus (3)

• Weighted Active Space Protocol for Multireference Machine-Learned Potentials
• Flexible Tuning of Asymmetric Near-field Radiative Thermal Transistor by Utilizing Distinct Phase Change Materials
• Viscosity, breakdown of Stokes-Einstein relation and dynamical heterogeneity in supercooled liquid Ge${}_2$Sb${}_2$Te${}_5$ from simulations with a neural network potential