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1.
Phys Chem Chem Phys ; 22(29): 16822-16830, 2020 Aug 07.
Artigo em Inglês | MEDLINE | ID: mdl-32662490

RESUMO

Creating effective explosives with improved performance and physical properties is a challenging task. There are different methods to achieve this - creating completely new individual high-energy compounds or changing the characteristics of the already known ones. Cocrystallization is one of the ways to improve the critical properties of energetic materials. In this work we show that the crystal structure of stable molecular crystals and cocrystals of energetic molecules can be studied using the evolutionary algorithm USPEX coupled with forcefields or ab initio calculations. Here we show this through tests on PETN, TNT, HMX, CL-20, and TATB, and we separately consider the following compositions of cocrystals: DNDAP + CL-20 (4 : 8) and BTF + CL-20 (4 : 4). As a result, we found cocrytals of the previously known compositions and also novel cocrystals, which might also be stable in the experiment.

2.
Sci Adv ; 4(10): eaat9776, 2018 10.
Artigo em Inglês | MEDLINE | ID: mdl-30333994

RESUMO

Hydrogen-rich hydrides attract great attention due to recent theoretical (1) and then experimental discovery of record high-temperature superconductivity in H3S [T c = 203 K at 155 GPa (2)]. Here we search for stable uranium hydrides at pressures up to 500 GPa using ab initio evolutionary crystal structure prediction. Chemistry of the U-H system turned out to be extremely rich, with 14 new compounds, including hydrogen-rich UH5, UH6, U2H13, UH7, UH8, U2H17, and UH9. Their crystal structures are based on either common face-centered cubic or hexagonal close-packed uranium sublattice and unusual H8 cubic clusters. Our high-pressure experiments at 1 to 103 GPa confirm the predicted UH7, UH8, and three different phases of UH5, raising confidence about predictions of the other phases. Many of the newly predicted phases are expected to be high-temperature superconductors. The highest-T c superconductor is UH7, predicted to be thermodynamically stable at pressures above 22 GPa (with T c = 44 to 54 K), and this phase remains dynamically stable upon decompression to zero pressure (where it has T c = 57 to 66 K).

3.
Sci Rep ; 7(1): 8512, 2017 08 17.
Artigo em Inglês | MEDLINE | ID: mdl-28819297

RESUMO

We used the machine learning technique of Li et al. (PRL 114, 2015) for molecular dynamics simulations. Atomic configurations were described by feature matrix based on internal vectors, and linear regression was used as a learning technique. We implemented this approach in the LAMMPS code. The method was applied to crystalline and liquid aluminum and uranium at different temperatures and densities, and showed the highest accuracy among different published potentials. Phonon density of states, entropy and melting temperature of aluminum were calculated using this machine learning potential. The results are in excellent agreement with experimental data and results of full ab initio calculations.

4.
J Phys Chem A ; 121(16): 3019-3027, 2017 Apr 27.
Artigo em Inglês | MEDLINE | ID: mdl-28351144

RESUMO

Energetic materials are characterized by fast and complex chemical reactions. It makes them hardly available for kinetic experiments in relevant conditions and a good target for reactive molecular dynamics simulations. In this work, unimolecular and condensed-phase thermal decomposition of pentaerythritol tetranitrate (PETN) are investigated by ReaxFF molecular dynamics. It is shown that the decomposition kinetics in condensed phase may be described with the activation barrier lower by a factor of 2 than that for isolated molecules. The effect of the intermolecular hydrogen transfer is revealed in condensed phase. Energetic barriers for hydrogen transfer in two energetic materials (methyl nitrate, which is a nitroester as well as PETN, and o-nitrotoluene) are studied with ReaxFF and DFT using nudged elastic band technique. The results indicate that ReaxFF gives significantly lower activation energy for intermolecular hydrogen transfer in nitroesters than different DFT approximations, which explains the molecular dynamics results for PETN.

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