Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

© 2018 Informa UK Limited, trading as Taylor & Francis Group. 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

Monatomic phase change memory

Phase change memory has been developed into a mature technology capable of storing information in a fast and non-volatile way, with potential for neuromorphic computing applications. However, its future impact in electronics depends crucially on how the materials at the core of this technology adapt to the requirements arising from continued scaling towards higher device densities. A common strategy to finetune the properties of phase change memory materials, reaching reasonable thermal stability in optical data storage, relies on mixing precise amounts of different dopants, resulting often in quaternary or even more complicated compounds. Here we show how the simplest material imaginable, a single element (in this case, antimony), can become a valid alternative when confined in extremely small volumes. This compositional simplification eliminates problems related to unwanted deviations from the optimized stoichiometry in the switching volume, which become increasingly pressing when devices are aggressively miniaturized. Removing compositional optimization issues may allow one to capitalize on nanosize effects in information storage

Electronic, optical and thermal properties of the hexagonal and fcc Ge2Sb2Te5 chalcogenide from first-principle calculations

We present a comprehensive computational study on the properties of face-centered cubic and hexagonal chalcogenide Ge2Sb2Te5. We calculate the electronic structure using density functional theory (DFT); the obtained density of states (DOS) compares favorably with experiments, also looking suitable for transport analysis. Optical constants including refraction index and absorption coefficient capture major experimental features, aside from an energy shift owed to an underestimate of the band gap that is typical of DFT calculations. We also compute the phonon DOS for the hexagonal phase, obtaining a speed of sound and thermal conductivity in good agreement with the experimental lattice contribution. The calculated heat capacity reaches ~ 1.4 x 106 J/(m3 K) at high temperature, in agreement with experimental data, and provides insight into the low-temperature range (< 150 K), where data are unavailable.Comment: 19 pages, 8 figure