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Atomistic simulations of the crystallization of amorphous GeTe nanoparticles

Debdipto Acharya1, Omar Abou El Kheir1, Simone Perego1, Davide Campi1, Marco Bernasconi1*

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

* Corresponding authors emails: marco.bernasconi@unimib.it
DOI10.24435/materialscloud:gx-k3 [version v1]

Publication date: Oct 23, 2024

How to cite this record

Debdipto Acharya, Omar Abou El Kheir, Simone Perego, Davide Campi, Marco Bernasconi, Atomistic simulations of the crystallization of amorphous GeTe nanoparticles, Materials Cloud Archive 2024.172 (2024), https://doi.org/10.24435/materialscloud:gx-k3

Description

The effect of dimensionality reduction on the crystallization kinetics of phase change materials is of relevance for the operation of ultrascaled memory devices. Therefore, the crystallization of amorphous nanoparticles (NPs) of the prototypical phase change compounds, GeTe and Ge₂Sb₂Te₅, has been addressed by several experimental works in recent years. In this work, we performed molecular dynamics simulations of the crystallization process of amorphous GeTe NPs with diameter in the range 3-6 nm (512-4096 atoms) by exploiting a machine-learned interatomic potential. We saw a few crystal nucleation events in the larger NPs but no crystallization in the smallest NP, 3 nm in diameter, in simulations lasting up to 80 ns in the temperature range 500-750 K. The analysis of the crystallization kinetics suggests that the nucleation rate per volume decreases with the NP size to an extent that prevents us from seeing crystallization in the smallest NP on our simulation time scale. This result is consistent with the large raise in crystallization temperature observed experimentally for NPs with diameters shorter than 3.5 nm.

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License

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External references

Preprint
D. Acharya, O. Abou El Kheir, S. Perego, D. Campi, M. Bernasconi, The Journal of Physical Chemistry C, (2024)

Keywords

Neural Network Potential Crystallization Phase Change Materials GeTe molecular dynamics simulation Neuromophic Computing

Version history:

2024.172 (version v1) [This version] Oct 23, 2024 DOI10.24435/materialscloud:gx-k3