Statistical analysis of homogeneous nucleation of metallic nanoparticles during gas-phase synthesis.

In this work, we set out to develop a model of gas-phase nucleation in a mixture of copper and argon atoms, which can be further used for analysing macro-systems. Processes occurring at the atomic level are described using coefficients obtained by statistical analysis of molecular dynamic (MD) data on interactions of metal clusters with metal and argon atoms. The MD simulation results are compared with those obtained using the proposed macroscopic model. It is found that the coefficients obtained by averaging the interaction data suitably represent the integral value of the heat of condensation, although result in the smoothing of the energy distribution functions of the clusters. Analysis of the evolution of the number of clusters has shown that the values of their increase rate were lower than those obtained by MD simulation. The conclusion is made, that in order to improve the precision of the developed gas-phase condensation model, it should be supplemented by cluster coagulation.

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.

Analysis of the electronic structure of liquid rubidium by the methods of ab initio molecular dynamics, linear muffin-tin orbitals and recursion.

It is well known that liquid rubidium shows some unusual properties at low densities. The ab initio SIESTA package and the supercell technique within the linear muffin-tin orbital method were used to investigate this phenomenon. Electronic structures of liquid rubidium at different temperatures from the melting point up to the critical point were obtained. The atomic structure for the supercell technique was simulated for a cluster of 4000 atoms by the Schommers method on the basis of experimental structure factors of Rb obtained by Tamura and co-workers at different temperatures (from 373 up to 1973 K). The Kubo-Greenwood formula was applied for the calculations of the melt conductivity. The results obtained indicate that the metal-nonmetal transition in liquid rubidium is not connected to the gap at the Fermi energy in the density of electronic states, but, more likely, with electron localization on some kind of atomic cluster.