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Understanding the thermal properties of disordered systems is of fundamental importance for condensed matter physics - and for practical applications as well. While quantities such as the thermal conductivity are usually well characterised experimentally, their microscopic origin is often largely unknown - hence the pressing need for molecular simulations. However, the time and length scales involved with thermal transport phenomena are typically well beyond the reach of ab initio calculations. On the other hand, many amorphous materials are characterised by a complex structure, which prevents the construction of classical interatomic potentials. One way to get past this deadlock is to harness machine-learning (ML) algorithms to build interatomic potentials: these can be nearly as computationally efficient as classical force fields while retaining much of the accuracy of first-principles calculations. Here, we discuss neural network potentials (NNPs) and Gaussian approximation potentials (GAPs), two popular ML frameworks. We review the work that has been devoted to investigate, via NNPs, the thermal properties of phase-change materials, systems widely used in non-volatile memories. In addition, we present recent results on the vibrational properties of amorphous carbon, studied via GAPs. In light of these results, we argue that ML-based potentials are among the best options available to further our understanding of the vibrational and thermal properties of complex amorphous solids. [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]

A glimpse into amorphous materials. In their Communication on page 10817 ff. R. Mazzarello, R. Dronskowski et al. investigate the bonding nature of amorphous GeTe, a prototypical phase‐change data‐storage material. Starting from a molecular‐dynamics snapshot, the role of different structural motifs is elucidated by the use of new tools; this way, the chemical “language” of bonding and coordination polyhedra may be transferred to the amorphous state. [ABSTRACT FROM AUTHOR]