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.

本研究开发了一款适用于富锗GexTe合金的机器学习原子间势（machine-learned interatomic potential），旨在揭示这类材料的相分离与结晶动力学过程。该研究成果可为嵌入式相变存储器的研发与应用提供支撑——这类存储器通过富集GeSbTe合金中的锗元素以提升其结晶温度。该原子间势通过拟合大规模数据库得到，该数据库包含基于密度泛函理论（Density Functional Theory）计算得到的能量与原子受力数据，并采用DeePMD-kit工具包实现的神经网络方案完成训练。该原子间势具备高精度特性，可准确描述从纯锗、化学计量比GeTe到富锗Ge₂Te合金的宽组分范围体系的液态、非晶态与晶态相的结构及动力学性质。大规模分子动力学模拟（molecular dynamics simulations）揭示了温度依赖的结晶机制：在600 K下，过量锗的偏析现象可在纳秒时间尺度内发生，随后近乎化学计量比的GeTe区域完成结晶；在500 K下，晶态GeTe的成核过程先于相分离发生，随后因过量锗的持续排出，晶体生长过程较为缓慢。

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。