The phase change compound Ge2Sb2Te5 (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 10,000 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.

Abou El Kheir, O., Bonati, L., Parrinello, M., Bernasconi, M. (2024). Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential. NPJ COMPUTATIONAL MATERIALS, 10(1) [10.1038/s41524-024-01217-6].

Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential

Abou El Kheir, O;Bernasconi, M
2024

Abstract

The phase change compound Ge2Sb2Te5 (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 10,000 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.
Articolo in rivista - Articolo scientifico
phase change materials, molecular dynamics, electronic memories, machine learning
English
10-feb-2024
2024
10
1
33
open
Abou El Kheir, O., Bonati, L., Parrinello, M., Bernasconi, M. (2024). Unraveling the crystallization kinetics of the Ge2Sb2Te5 phase change compound with a machine-learned interatomic potential. NPJ COMPUTATIONAL MATERIALS, 10(1) [10.1038/s41524-024-01217-6].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/462402
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