Dario Baratella^1*, Omar Abou El Kheir^1*, Marco Bernasconi^1*

1 Department of Materials Science, University of Milano-Bicocca, Via R. Cozzi 55, Milano, I-20125, Italy

* Corresponding authors emails: d.baratella@campus.unimib.it, o.abouelkheir@campus.unimib.it, marco.bernasconi@unimib.it

DOI10.24435/materialscloud:cf-tq [version v1]

Publication date: Dec 09, 2024

How to cite this record

Dario Baratella, Omar Abou El Kheir, Marco Bernasconi, Crystallization kinetics in Ge-rich Ge_xTe alloys from large scale simulations with a machine-learned interatomic potential, Materials Cloud Archive 2024.195 (2024), https://doi.org/10.24435/materialscloud:cf-tq

Description

A machine-learned interatomic potential for Ge-rich Ge_xTe 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.

Materials Cloud sections using this data

Files

File name	Size	Description
NN_Potential_Ge-rich_GeTe.zip MD5md5:39680a385503c39326f99bc6871d5c71	26.2 MiB	DeePMD potential for Ge-rich GeTe
Database.zip MD5md5:65df1be2123053a63097d14d6e8390a5	747.9 MiB	The DFT database used to fit the NN potential consists of configurations of Ge, GeTe and Ge-rich GeTe (Ge2Te, Ge3Te and Ge9Te) divided into crystalline, amorphous, liquid and supercooled liquid phases. The configurations of Ge2Te are further divided into configurations in a 100-atom cell (Ge2Te_100), exotic high-energy configurations (Ge2Te_Ex and Ge2Te_Ex2) and a-Ge/a-GeTe interface configurations in a 300-atom cell (Ge2Te_Int) as described in the paper.
video.zip MD5md5:fe2932d060c19beb81664b765f1be387	965.7 MiB	Videos of the crystallization process at 600 K and 500 K of Ge2Te and GeTe, of the phase separation process of Ge2Te at 600 K and of the annealing at 1200 K of the phase segregated and crystallized model of Ge2Te
trajectories.zip MD5md5:20e3ac317ccea43408f67cb3b6935fff	1.3 GiB	Trajectories files of the crystallization process at 600 K and 500 K of Ge2Te and GeTe and of the annealing at 1200 K of the phase segregated and crystallized model of Ge2Te

License

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External references

Keywords

phase change materials molecular dynamics machine learning electronic memories crystallization