Crystallization kinetics in Ge-rich GexTe alloys from large scale simulations with a machine-learned interatomic potential

A machine-learned interatomic potential for Ge-rich GexTe 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 Ge₂Te alloy. Large scale molecular dynamics simulations revealed a crystallization mechanism which depends on temperature. At 600 K, segregation of most of Ge in excess was observed to occur on the ns time scale followed by crystallization of nearly stoichiometric GeTe regions. At 500 K, nucleation of crystalline GeTe was observed to occur before phase separation, followed by a slow crystal growth due to the concurrent expulsion of Ge in excess.

搜集汇总

背景与挑战

背景概述

该数据集基于机器学习开发的原子间势能，研究富Ge的GexTe合金中的结晶动力学，适用于多种成分的液态、非晶和晶相分析。通过大规模分子动力学模拟，揭示了温度依赖的结晶机制：高温下Ge先分离后结晶，低温下结晶先发生再缓慢生长。数据集包含模拟文件和相关资源，旨在支持相变存储器材料的研究。