On phase separation and crystallization of Ge-rich GeSbTe alloys from atomistic simulations with a machine learning interatomic potential

We developed a machine learning interatomic potential (MLIP) for Ge-rich GeSbTe alloys of interest for applications in phase change memories embedded in microcontrollers.

Materials Science Engineering Computer Science Physics

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References (63)

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02Viscosity, breakdown of Stokes-Einstein relation, and dynamical heterogeneity in supercooled liquid Ge2Sb2Te5 from simulations with a neural network potential.Simone Marcorini, Rocco Pomodoro, O. Abou El Kheir et al.2025 · Journal of Chemical Physics · 3 citations

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