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Studies of supercooled liquid phase‐change materials are important for the development of phase‐change memory and neuromorphic computing devices. Herein, a machine‐learning (ML)‐based interatomic potential for Ge2Sb2Te5 (GST) to conduct large‐scale molecular dynamics simulations of liquid and supercooled liquid GST is used. A pronounced effect of the thermostat parameters on the simulation results is demonstrated, and it is shown how using a Langevin thermostat with optimized damping values can lead to excellent agreement with reference ab initio molecular dynamics (AIMD) simulations. Structural and dynamical analyses are presented, including the studies of radial and angular distributions, homopolar bonds, and the temperature‐dependent diffusivity. Herein, the usefulness of ML‐driven molecular dynamics for further studies of supercooled liquid GST, with length and timescales far exceeding those that are accessible to AIMD is demonstrated. [ABSTRACT FROM AUTHOR]

Despite its simple chemical constitution and unparalleled technological importance, the phase-change material germanium telluride (GeTe) still poses fundamental questions. In particular, the bonding mechanisms in amorphous GeTe have remained elusive to date, owing to the lack of suitable bond-analysis tools. Herein, we introduce a bonding indicator for amorphous structures, dubbed 'bond-weighted distribution function' (BWDF), and we apply this method to amorphous GeTe. The results underline a peculiar role of homopolar GeGe bonds, which locally stabilize tetrahedral fragments but not the global network. This atom-resolved (i.e., chemical) perspective has implications for the stability of amorphous 'zero bits' and thus for the technologically relevant resistance-drift phenomenon. [ABSTRACT FROM AUTHOR]