Yazar "Merinov, Valeriy B." seçeneğine göre listele

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    All-Nitrogen Cages and Molecular Crystals: Topological Rules, Stability, and Pyrolysis Paths
    (Mdpi, 2020) Katin, Konstantin P.; Merinov, Valeriy B.; Kochaev, Alexey I.; Kaya, Savas; Maslov, Mikhail M.
    We combined ab initio molecular dynamics with the intrinsic reaction coordinate in order to investigate the mechanisms of stability and pyrolysis of N-4 divided by N-120 fullerene-like nitrogen cages. The stability of the cages was evaluated in terms of the activation barriers and the activation Gibbs energies of their thermal-induced breaking. We found that binding energies, bond lengths, and quantum-mechanical descriptors failed to predict the stability of the cages. However, we derived a simple topological rule that adjacent hexagons on the cage surface resulted in its instability. For this reason, the number of stable nitrogen cages is significantly restricted in comparison with their carbon counterparts. As a rule, smaller clusters are more stable, whereas the earlier proposed large cages collapse at room temperature. The most stable all-nitrogen cages are the N-4 and N-6 clusters, which can form the van der Waals crystals with densities of 1.23 and 1.36 g/cm(3), respectively. The examination of their band structures and densities of electronic states shows that they are both insulators. Their power and sensitivity are not inferior to the modern advanced high-energy nanosystems.
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    Probing of Neural Networks as a Bridge from Ab Initio Relevant Characteristics to Differential Scanning Calorimetry Measurements of High-Energy Compounds
    (Wiley-V C H Verlag Gmbh, 2022) Bondarev, Nikolay, V; Katin, Konstantin P.; Merinov, Valeriy B.; Kochaev, Alexey, I; Kaya, Savas; Maslov, Mikhail M.
    The relationships between the theoretical values calculated using density functional theory and experimental data derived from the differential scanning calorimetry of high-energy organic compounds are studied. The theoretical values are the number of atoms and bonds of different types and their lengths, minimum eigenfrequencies, atomization energies, ionization potentials, electron affinities, and frontier orbital energies. The experimental data are the amounts of releasing heat (the first peaks higher than 1 kJ g(-1)) and corresponding temperatures. Neural networks and regression, factor, discriminant, and cluster analysis are applied to find the dependencies between theoretical values and experimental data. It is found that the heat amount cannot be predicted in the general cases, whereas the corresponding temperature can be predicted with a neural network with an accuracy of approximate to 30 degrees C. Cluster and discriminant analysis provides the way for the classification of high-energy compounds into three groups. Some of these groups require particular rules for the prediction of experimental data from the theoretical values.