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

Ice formation on kaolinite: Insights from molecular dynamics simulations

The formation of ice affects many aspects of our everyday life as well as important technologies such as cryotherapy and cryopreservation. Foreign substances almost always aid water freezing through heterogeneous ice nucleation, but the molecular details of this process remain largely unknown. In fact, insight into the microscopic mechanism of ice formation on different substrates is difficult to obtain even if state-of-the-art experimental techniques are used. At the same time, atomistic simulations of heterogeneous ice nucleation frequently face extraordinary challenges due to the complexity of the water-substrate interaction and the long time scales that characterize nucleation events. Here, we have investigated several aspects of molecular dynamics simulations of heterogeneous ice nucleation considering as a prototypical ice nucleating material the clay mineral kaolinite, which is of relevance in atmospheric science. We show via seeded molecular dynamics simulations that ice nucleation on the hydroxylated (001) face of kaolinite proceeds exclusively via the formation of the hexagonal ice polytype. The critical nucleus size is two times smaller than that obtained for homogeneous nucleation at the same supercooling. Previous findings suggested that the flexibility of the kaolinite surface can alter the time scale for ice nucleation within molecular dynamics simulations. However, we here demonstrate that equally flexible (or non flexible) kaolinite surfaces can lead to very different outcomes in terms of ice formation, according to whether or not the surface relaxation of the clay is taken into account. We show that very small structural changes upon relaxation dramatically alter the ability of kaolinite to provide a template for the formation of a hexagonal overlayer of water molecules at the water-kaolinite interface, and that this relaxation therefore determines the nucleation ability of this mineral

Modelling the interactions of NO in a-SiO2

Nitric oxide (NO) is often used for the passivation of SiC/SiO2 metal oxide semiconductor (MOS) devices. Although it is established experimentally, using XPS, EELS, and SIMS measurements, that the 4H-SiC/SiO2 interface is extensively nitridated, the mechanisms of NO incorporation and diffusion in amorphous (a)-SiO2 films are still poorly understood. We used Density Functional Theory (DFT) to simulate the diffusion of NO through a-SiO2 and correlate local steric environment in amorphous network to interstitial NO (NOi) incorporation energy and migration barriers. Using an efficient sampling technique we identify the energy minima and transition states for neutral and negatively charged NOi molecules. Neutral NO interacts with the amorphous network only weakly with the smallest incorporation energies in bigger cages. On the other hand NOi -1 binds at the intrinsic precursor sites for electron trapping

Ice is born in low-mobility regions of supercooled liquid water

When an ice crystal is born from liquid water, two key changes occur: (i) The molecules order and (ii) the mobility of the molecules drops as they adopt their lattice positions. Most research on ice nucleation (and crystallization in general) has focused on understanding the former with less attention paid to the latter. However, supercooled water exhibits fascinating and complex dynamical behavior, most notably dynamical heterogeneity (DH), a phenomenon where spatially separated domains of relatively mobile and immobile particles coexist. Strikingly, the microscopic connection between the DH of water and the nucleation of ice has yet to be unraveled directly at the molecular level. Here we tackle this issue via computer simulations which reveal that (i) ice nucleation occurs in low-mobility regions of the liquid, (ii) there is a dynamical incubation period in which the mobility of the molecules drops before any ice-like ordering, and (iii) ice-like clusters cause arrested dynamics in surrounding water molecules. With this we establish a clear connection between dynamics and nucleation. We anticipate that our findings will pave the way for the examination of the role of dynamical heterogeneities in heterogeneous and solution-based nucleation

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

Raman spectra of cubic and amorphous Ge2Sb2Te5 from first principles

We computed the Raman spectrum of cubic and amorphous Ge2Sb2Te5 (GST) by ab initio phonons and an empirical bond polarizability model. Models of the amorphous phase were generated by quenching from the melt by means of ab initio molecular dynamics simulations. The calculated spectra are in good agreement with experimental data which confirms the reliability of the models of the amorphous phase emerged from the simulations. All the features of the spectrum in both crystalline and amorphous GST can be assigned to vibrations of defective octahedra. The calculations reveal that the polarizability of the Sb-Te is much higher than that of Ge-Te bonds and of Ge-Ge/Sb wrong bonds resulting in a much lower Raman response of tetrahedra which are made of Ge-Te and wrong bonds. As a consequence and as opposed to amorphous GeTe, the signatures of tetrahedra in the Raman spectrum of amorphous GST are hidden by the larger Raman cross section of defective octahedra

Atomistic simulations of thermal conductivity in GeTe nanowires

The thermal conductivity of GeTe crystalline nanowires has been computed by means of non-equilibrium molecular dynamics simulations employing a machine learning interatomic potential. This material is of interest for application in phase change non-volatile memories. The resulting lattice thermal conductivity of an ultrathin nanowire (7.3 nm diameter) of 1.57 W m-1 K-1 is sizably lower than the corresponding bulk value of 3.15 W m-1 K-1 obtained within the same framework. The analysis of the phonon dispersion relations and lifetimes reveals that the lower thermal conductivity in the nanowire is mostly due to a reduction in the phonon group velocities. We further predict the presence of a minimum in the lattice thermal conductivity for thicker nanowires