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There has existed for a long time a paradigm that TiO phases at ambient conditions are stable only if structural vacancies are available. Using an evolutionary algorithm, we perform an ab initio search of possible zero-temperature polymorphs of TiO in wide pressure interval. We obtain the Gibbs energy of the competing phases taking into account entropy via quasiharmonic approximation and build the pressure–temperature diagram of the system. We reveal that two vacancy-free hexagonal phases are the most stable at relatively low temperatures in a wide range of pressures. The transition between these phases takes place at 28 GPa. Only above 1290 K at ambient pressure the phases with vacancies (B1-derived) become stable. In particular, the high-pressure hexagonal phase is shown to have unusual electronic properties, with a pronounced pseudo-gap in the electronic spectrum. The comparison of DFT–GGA and GW calculations demonstrates that the account for many-body corrections significantly changes the electronic spectrum near the Fermi energy. [ABSTRACT FROM AUTHOR]

• Atomistic simulations were conducted to study the decay of a superheated AlCu alloy. • The AlCu model was constructed using an artificial neural network machine-learning approach. • The Classical Nucleation Theory accurately described the melting of the AlCu alloy, without any adjustable parameters. The validity of the Classical Nucleation Theory (CNT), the standard tool for describing and predicting nucleation kinetics in metastable systems, has been under scrutiny for almost a century. While the CNT is commonly employed to describe liquid → crystal and liquid ↔ vapor phase transitions, its application to the crystal → liquid case has been limited because of the experimental challenges in achieving superheating states and detecting homogeneous liquid nucleation. In this study, we performed comprehensive molecular dynamics (MD) simulations of spontaneous melting of a superheated AlCu crystal under atmospheric pressure at five temperatures, covering a superheating range of T / T L = 1.1–1.3, where T L is the liquidus temperature. Two realistic AlCu models were investigated: one described by the modified embedded atom method (MEAM) and the other by an interatomic potential generated by an artificial neural network machine learning (ML) approach, extensively trained on an ab initio dataset of liquid and crystal configurations. Fifty independent melting events were simulated at each temperature. By analyzing the distribution of melting times using the Poisson law, the homogeneous nucleation rate was determined through the mean lifetime method. Additionally, the Zeldovich factor, critical nucleus size, and work of formation were obtained using the mean first-passage time method, utilizing the disorder parameter based on atomic displacements (liquid-like atoms in the superheated crystal) as the reaction coordinate. Also, the effective atomic transport coefficient across the metastable crystal/critical liquid nucleus interface was determined by MD simulations as the interfacial attachment coefficient for nuclei growth rates. Using these simulation-generated data, the theoretical nucleation rates were calculated by the CNT with no fitting parameters. We found excellent agreement between the theoretically and MD-computed liquid nucleation rates for both MEAM and ML crystals. Notably, the effective solid-liquid interfacial free energy value obtained from the MD data aligns with its recent experimental measure. Moreover, the CNT qualitatively and quantitatively described the underlying details of liquid drop nucleation in our ML solid, unprecedentedly and accurately reproducing the kinetic prefactor and the size, formation energy, and growth rate of the critical nuclei. Thus, the melting of the AlCu model created through machine learning-processed quantum calculations, that is, not relying on hand-crafted interatomic potential functions, was successfully described by the CNT phenomenological formalism, without any adjustable parameters. This finding confirms the CNT as a very reliable descriptor of homogeneous nucleation in the superheated AlCu alloy and generalizes this theory as a powerful tool for analyzing and predicting the kinetics of crystal-liquid transitions. [ABSTRACT FROM AUTHOR]

• Deep machine learning potential is applied for simulating viscosity of Al-Cu–Ni melts. • Results match experimental viscosity dependencies on temperature and concentration. • Simulations reproduce the minimum of the viscosity at the eutectic composition. • Deep machine learning potentials predict viscosity of multicomponent metallic melts. Calculating viscosity in multicompoinent metallic melts is a challenging task for both classical and ab initio molecular dynamics simulations methods. The former may not to provide enough accuracy and the latter is too resources demanding. Machine learning potentials provide optimal balance between accuracy and computational efficiency and so seem very promising to solve this problem. Here we address simulating kinematic viscosity in ternary Al-Cu–Ni melts with using deep neural network potentials (DP) as implemented in the DeePMD-kit. We calculate both concentration and temperature dependencies of kinematic viscosity in Al-Cu–Ni and conclude that the developed potential allows one to simulate viscosity with high accuracy; the deviation from experimental data does not exceed 12% and is close to the uncertainty interval of experimental data. More importantly, our simulations reproduce minimum on concentration dependency of the viscosity at the eutectic composition. Thus, we conclude that DP-based MD simulations is highly promising way to calculate viscosity in multicomponent metallic melts. [ABSTRACT FROM AUTHOR]

In recent years, A-site ordered half-doped double-perovskite manganites RBaMn 2 O 6 (R=rare earth) have attracted much attention due to their remarkable physical properties and a prospect of application as magnetoresistive, multiferroic, and oxygen storage materials. The nature of the ground state in RBaMn 2 O 6 as well as sequence of phase transitions taking place at cooling are not yet well understood due to complexity in both experimental and theoretical studies. Here we address the origin of the ground-state structure in PrBaMn 2 O 6 as well as its electronic and magnetic properties. Utilizing GGA+ U approach and specially designed strategy to perform structural optimization, we show that the system has two competing AFM-A and AFM-CE magnetic structures with very close energies. The AFM-A structure is a metal, while AFM-CE is an insulator and the transition to the insulating state is accompanied by the charge Mn3+/Mn4+, and orbital 3 x 2 − r 2/3 y 2 − r 2 orderings. This orbital ordering results in strong cooperative Jahn-Teller (JT) distortions, which lower the crystal symmetry. Our findings give a key to understanding contradictions in available experimental data on PrBaMn 2 O 6 and open up the prospects to theoretical refinements of ground-state structures in other RBaMn 2 O 6 compounds. • RBaMn2O6 has two competing AFM-A and AFM-CE magnetic structures. • AFM-A structure is a metallic while AFM-CE is an insulating. • Metal-Insulator transition is accompanied by charge (Mn3+/Mn4+) and orbital (3x2 -r2 /3y2 -r2) orderings. • Strong cooperative Jahn-Teller distortions in AFM-CE configuration lead to P2_1/c structure. [ABSTRACT FROM AUTHOR]

The prediction of phase composition in metallic alloys is one of the main challenges in modern material science. The most effective and promising methods to solve this problem are currently based on machine learning (ML) algorithms. The most urgent issues in developing such methods are the choice of input features (descriptors) and the search for the most effective ML models. Here we address these issues for the problem of phase composition prediction in high-entropy alloys (HEAs). We combine two ideas recently proposed in this field: the use of genetic algorithms to search for optimal sets of descriptors and a multi-label classification scheme. By using this approach, we achieve the value of balanced accuracy of more than 91% in the prediction of selected phases. • Genetic algorithms were used for searching optimal descriptors. • Effectiveness of three different sets of ANN descriptors was compared. • Accuracy of multi-label and multi-class ANN was compared. • Multi-label ANN with evolutionary designed descriptors demonstrates accuracy 91%. [ABSTRACT FROM AUTHOR]

• Crystal structure, optical and magnetic properties of PrBaMn 2 O 5.94 are studied. • The Curie and Néel temperatures T C = 305 K, T N = 262 K are determined. • Structural phase transition of the first order at 200 K is found. • Electronic structure above and below structural phase transition is calculated. • Structural transition is caused by Mn e g states splitting at MI-transition. [Display omitted] Double manganite PrBaMn 2 O 6 with ordered alternate stacking of PrMnO 3 /BaMnO 3 layers attracts a special interest due to presence of two magnetic phase transitions close to room temperature; that makes it perspective magnetocaloric and magnetoresistive material. Besides the magnetic phase transition, the structural transition accompanied with the elongation of the a -axis and the contraction of the c -axis was experimentally detected. The origin of structural phase transition is studied by X-ray powder diffraction, X-ray photoelectron spectroscopy (XPS), IR absorption spectroscopy, magnetometry and dynamical mean-field theory (DMFT) for electronic structure calculations. It is found that the structural phase transition is caused by splitting of e g -doublet states due to the tendency towards orbital ordering at the metal-insulator phase transition. [ABSTRACT FROM AUTHOR]