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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]

Chitin is an abundant biopolymer that stabilizes the exoskeleton of insects and gives structure to plants. Its macroscopic properties go back to an intricate network of hydrogen bonds that connect the polymer strands, and these intermolecular links have been under ongoing study. Here, we use atomistic simulations to explore hydrogen bonding in the most abundant form, α-chitin. The crystal structure exhibits disorder, and so discrete models are systematically derived as suitable approximants to the macroscopic material. These models then allow us to perform dispersion-corrected density-functional theory (DFT-D) simulations on the three-dimensional crystal network and on lower-dimensional fragments. Thereby, we rationalize the nature of hydrogen bonding and the role of crystallographic disorder for the stability of α-chitin, and complement previous, larger-scale molecular-dynamics (MD) simulations as well as recent fiber-diffraction experiments. Our results provide new, atomic-level insight into one of Nature's most abundant building materials, and the techniques and concepts are likely transferable to other biopolymers. [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]

Machine learning (ML) methods are of rapidly growing interest for materials modeling, and yet, the use of ML interatomic potentials for new systems is often more demanding than that of established density-functional theory (DFT) packages. Here, we describe computational methodology to combine the CASTEP first-principles simulation software with the on-the-fly fitting and evaluation of ML interatomic potential models. Our approach is based on regular checking against DFT reference data, which provides a direct measure of the accuracy of the evolving ML model. We discuss the general framework and the specific solutions implemented, and we present an example application to high-temperature molecular-dynamics simulations of carbon nanostructures. The code is freely available for academic research. [ABSTRACT FROM AUTHOR]

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]

We study the deposition of tetrahedral amorphous carbon (ta-C) films from molecular dynamics simulations based on a machine-learned interatomic potential trained from density-functional theory data. For the first time, the high sp³ fractions in excess of 85% observed experimentally are reproduced by means of computational simulation, and the deposition energy dependence of the film's characteristics is also accurately described. High confidence in the potential and direct access to the atomic interactions allow us to infer the microscopic growth mechanism in this material. While the widespread view is that ta-C grows by "subplantation," we show that the so-called "peening" model is actually the dominant mechanism responsible for the high sp³ content. We show that pressure waves lead to bond rearrangement away from the impact site of the incident ion, and high sp³ fractions arise from a delicate balance of transitions between three- and fourfold coordinated carbon atoms. These results open the door for a microscopic understanding of carbon nanostructure formation with an unprecedented level of predictive power. [ABSTRACT FROM AUTHOR]

We generate representative structural models of amorphous carbon (a-C) from constant-volume quenching from the liquid with subsequent relaxation of internal stresses in molecular dynamics simulations using empirical and machine-learning interatomic potentials. By varying volume and quench rate we generate structures with a range of density and amorphous morphologies. We find that all a-C samples show a universal relationship between hybridization, bulk modulus and density despite having distinctly different cohesive energies. Differences in cohesive energy are traced back to slight changes in the distribution of bond-angles that is likely linked to thermal stability of these structures. [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]