Deep machine learning interatomic potential for liquid silica.

The use of machine learning to develop neural network potentials (NNP) representing the interatomic potential energy surface allows us to achieve an optimal balance between accuracy and efficiency in computer simulation of materials. A key point in developing such potentials is the preparation of a training dataset of ab initio trajectories. Here we apply a deep potential molecular dynamics (DeePMD) approach to develop NNP for silica, which is the representative glassformer widely used as a model system for simulating network-forming liquids and glasses. We show that the use of a relatively small training dataset of high-temperature ab initio configurations is enough to fabricate NNP, which describes well both structural and dynamical properties of liquid silica. In particular, we calculate the pair correlation functions, angular distribution function, velocity autocorrelation functions, vibrational density of states, and mean-square displacement and reveal a close agreement with ab initio data. We show that NNP allows us to expand significantly the time-space scales achievable in simulations and thus calculating dynamical and transport properties with more accuracy than that for ab initio methods. We find that developed NNP allows us to describe the structure of the glassy silica with satisfactory accuracy even though no low-temperature configurations were included in the training procedure. The results obtained open up prospects for simulating structural and dynamical properties of liquids and glasses via NNP.

Ab initio molecular dynamics and high-dimensional neural network potential study of VZrNbHfTa melt.

The structural and dynamics properties of melts are directly related to their solidification processes, and consequently to the properties of as-cast solid alloys. Ab initio molecular dynamics (AIMD) is a powerful tool that can study both of these factors. However, the main disadvantage of this method is its low performance which is critical for simulation of the multicomponent liquids. At the same time the atomistic simulation of multicomponent liquids has found its application for prediction of the formation of high-entropy alloys-a novel class of materials with enhanced mechanical properties. An effective method to solve the problem of AIMD low performance may be the design of pair or many-body potentials for classical molecular dynamics. One of the promising approaches is high-dimensional neural networks-the method of constructing many-body potentials for classical molecular dynamics from ab initio data. Thus, in this work, the high-dimensional neural network potential for multicomponent liquid VZrNbHfTa melt was constructed. The structure of this melt obtained by AIMD and high-dimensional neural network potential was compared by analyzing partial radial distribution functions. Dynamics of the melt obtained by both methods was also compared analyzing velocity autocorrelation functions and mean-square displacement for each type of atom in multicomponent VZrNbHfTa melt. It was shown that structure and dynamics are reproduced well by high-dimensional neural network potential (HDNNP). Some differences between HDNNP- and AIMD-obtained structure and dynamics are explained by finite-size effect and lack of statistics in AIMD simulation along with inherent errors in energy and force estimations made by high-dimensional neural network potentials. Analysis of melt structure via partial radial distribution functions and chemical short range order parameters led to the conclusion that vanadium atoms are repulsed from all the atoms of another type in liquid VZrNbHfTa system, which lowers the probability of single phase disordered solid solution formation. Diffusivity in multicomponent melt was found to decrease with increasing mass and size of an atom.

Nonstoichiometric titanium dioxide nanotubes with enhanced catalytical activity under visible light.

The catalytic activity of nanotubular titanium dioxide films formed during the oxidation of acetone to carbon dioxide under the action of visible light with a wavelength of 450 nm was found to be approximately 2 times higher compared to standard titanium dioxide (Degussa P25). The nanotubular films were grown by the anodization of titanium foil using an original technique. Diffuse reflectance spectra of the films are attributed to enhanced activity in the visible spectrum by the nonstoichiometry of titanium dioxide near the interface between the nanotubular film and the titanium foil substrate.

Correction: High-temperature X-ray diffraction and thermal expansion of nanocrystalline and coarse-crystalline acanthite α-Ag2S and argentite ß-Ag2S.

Correction for 'High-temperature X-ray diffraction and thermal expansion of nanocrystalline and coarse-crystalline acanthite α-Ag2S and argentite ß-Ag2S' by S. I. Sadovnikov et al., Phys. Chem. Chem. Phys., 2016, 18, 4617-4626.

High-temperature X-ray diffraction and thermal expansion of nanocrystalline and coarse-crystalline acanthite α-Ag2S and argentite ß-Ag2S.

An in situ study of thermal expansion of polymorphic phases of coarse-crystalline and nanocrystalline silver sulfide - monoclinic acanthite α-Ag2S and cubic argentite ß-Ag2S - has been carried out for the first time using the high-temperature X-ray diffraction method. The temperature dependencies of the unit cell parameters of acanthite and argentite in the interval of 300-623 K have been determined, and the thermal expansion coefficients of acanthite and argentite have been found. It is shown that the observed difference in the thermal expansion coefficients for nano- and coarse-crystalline acanthite is due to the small particle size of nanocrystalline silver sulfide leading to the growth of anharmonicity of atomic vibrations. It is established by differential thermal analysis that a reversible polymorphic acanthite-argentite phase transformation takes place at ∼449-450 K and the phase transformation enthalpy is equal to ∼3.7-3.9 kJ mol(-1).

An in situ high-temperature scanning electron microscopy study of acanthite-argentite phase transformation in nanocrystalline silver sulfide powder.

For the first time, the α-Ag2S (acanthite)-ß-Ag2S (argentite) phase transformation in nanocrystalline and coarse-crystalline powders of silver sulfide has been observed in situ by the scanning electron microscopy method in real-time. The argentite crystals are formed on the surface of acanthite particles as a result of electron-beam heating. According to the differential thermal analysis data, the transformation occurs at a temperature of ∼449-450 K, and the enthalpy of transformation is equal to ∼3.7-3.9 kJ mol(-1). The presence of α-Ag2S (acanthite)-ß-Ag2S (argentite) phase transformation is confirmed in situ by high-temperature X-ray diffraction data.

Nonstoichiometry of nanocrystalline monoclinic silver sulfide.

Powders of silver sulfide have been synthesized by chemical bath deposition from aqueous solutions of silver nitrate and sodium sulfide in the presence of sodium citrate or EDTA-H2Na2. Colloid solutions have been prepared by a chemical condensation method from the same aqueous solutions. Synthesized silver sulfide nanopowders have a monoclinic (space group P21/c) acanthite-type structure but the occupancy of the metal sublattice sites by Ag atoms is smaller than 1. Unlike coarse-crystalline silver sulfide Ag2S, silver sulfide nanopowders with particles sizes of less than ∼50 nm are nonstoichiometric, contain vacant sites in the metal sublattice and have a composition of ∼Ag1.93S.

Quasielastic neutron scattering study of hydrogen motion in NbC(0.71)H(0.28).

In order to study the mechanism and parameters of H jump motion in the nonstoichiometric Nb carbides, we have performed quasielastic neutron scattering (QENS) measurements for NbC(0.71)H(0.28) over the temperature range 11- 475 K. Our results indicate that about 30% of H atoms in this system participate in a fast diffusive motion. The temperature dependence of the corresponding H jump rate in the range 298-475 K follows the Arrhenius law with an activation energy of 328 ± 9 meV. The Q dependence of the QENS data suggests that the observed jump motion corresponds to long-range diffusion of H atoms along chains of the off-centre sites in carbon vacancies.

Identification of lattice vacancies on the two sublattices of SiC.

The identification of atomic defects in solids is of pivotal interest for understanding atomistic processes and solid state properties. Here we report on the exemplary identification of vacancies on each of the two sublattices of SiC by making use of (i) electron irradiation, (ii) measurements of the positron lifetimes, (iii) coincident Doppler broadening studies of the positron-electron annihilation radiation, and (iv) a comparison of the experimental data with theoretical studies. After 0.3 MeV electron irradiation, carbon vacancies V(C) are identified, where, after 0.5 MeV electron irradiation, predomi-nantly silicon vacancies V(Si) are observed. After 2.5 MeV irradiation, divacancies V(Si)-V(Si) are detected. The present results are expected to be of general importance for reliable identification of defects and atomic processes in complex solids.