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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]

Phase‐change materials (PCMs) are an intriguing class of materials that enable non‐volatile data storage and other technological applications. To gain a thorough understanding of PCMs, a variety of advanced experimental techniques is routinely used, but theory and computer simulation have long been of central importance as well. In their Review @ RRL (article no. 1800579), Konze et al. describe the role that orbital‐based chemical‐bonding analyses have played in PCM research: they can be used to identify stabilizing ("bonding") and de‐stabilizing ("antibonding") interactions between atoms using local‐orbital basis sets or (nowadays) a projection from plane‐wave DFT output. Besides a concise overview of the methodology and a survey of applications to PCMs, Konze et al. also highlight recent applications in other materials classes, aiming to make new links to the PCM field—and so to enable new microscopic insight into the crystalline and amorphous structures of these important materials. [ABSTRACT FROM AUTHOR]