Alloys with high configurational entropy fundamentally differ from traditional alloys as they contain not one, but five or more basic elements with concentrations of 5 to 35 at.% each. Owing to the unique elemental composition, high-entropy alloys can have unusual functional properties such as high strength and wear resistance, exceptional heat resistance, good structural stability and good resistance to corrosion and oxidation. The review considers methods of synthesis of high-entropy alloys, methods and results of computer simulation of the high-entropy alloy structures and applications of such alloys as corrosion-, heat- and radiation-resistant materials and biomedical materials. Particular attention is paid to the prospects for the use of such alloys as protective coatings. The bibliography includes 352 references.

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.

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.