Катин МОЛЕЦУЛАР ДЫНАМИЦС ИН МУЛТИСЦАЛЕ МОДЕЛИНГ 2015

rives the mean time between the two consequent potential energy fluctuations. After that, one performs MD calculations starting from the initial states with heightened potential energy that can turn into decomposition with high probability (the second level). If the system comes back to the state with the low potential energy (lower than Uc), then one again generates the state with the heightened potential (for example, using the Monte-Carlo method). As a result, the time required for the thermal decay is defined as the number of second-level attempts multiplied by the mean time between the two consequent potential energy fluctuations (see Fig. 9).

II. MD/diffusion multiscale simulation

Imagine that we need to simulate the diffusion of the perfume drop in the room. Direct MD simulations is impossible in this case, because the drop consists of ~ 1022 atoms. On the other hand, the problem can be easily solved using the Fick’s first law in the frame of diffusion approximation. In line with the Fick’s first law, the diffusion flux J is propor-

where D is the diffusion coefficient that defines the diffusion time scale and depends on the perfume molecule shape. To define D, MD simulations of the isolated perfume molecule in the thousands of molecules of air should be performed (basic level). Next, using the D value obtained, the concentration of the perfume inside the room n(x, y, z, t) can be derived from the Equation (24) (second level). In principle, the concentration distribution obtained can be used for the human’s smell modeling (the third level).

III. Multistage reaction

In chemical technologies multistage reactions are often regarded (methane cracking to acetylene is a good example). In such reactions reactants partially transform to the intermediate products. These intermediate products react with the initial reactants and produce the other intermediate products. This process is repeated until the final products will be obtained. In practice, the number of elementary reactions (stages) in some important petrochemical reactions may reach a few hun-

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dreds. Full MD simulation of such process that takes into account all these stages requires great computational costs. In that case, multiscale approach can reduce these costs by considering the kinetics of every stage separately and deriving the corresponding kinetic parameters at a given stage. The values of these parameters may be further used in the simpler kinetic models to evaluate the rate of the initial multistage reaction.

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Additional literature

1.Computer Simulations in Condensed Matter Systems: From Materials to Chemical Biology. Vol. 1 / Edited by M. Ferrario, G. Ciccotti and K. Binder. Springer Berlin Heidelberg, 2006.

2.Long-Time Protein Folding Dynamics from Short-Time Molecular Dynamics Simulations / J.D. Chodera, W.C. Swope, J.W. Pitera, K.A. Dill // Multiscale Modeling&Simulation. 2006. Vol. 5(4). P. 1214– 1226.

3.A multi-scale method for dynamics simulation in continuum solvent models. I: Finite-difference algorithm for Navier–Stokes equation / L. Xiao, Q. Cai, Z. Li, H. Zhao, R. Luo // Chemical Physics Letters. 2014. Vol. 616–617. P. 67–74.

4.J. Lee, C. Basaran. A multiscale modeling technique for bridging molecular dynamics with finite element method // Journal of Computational Physics. 2013. Vol. 253. P. 64–85.

T.L. Windus, M. Dupuis and J.A. Montgomery // Journal of Computational Chemistry. 1993. Vol. 14. P. 1347–1363.

6. CLUSTVIS1: a new software package for visualization of atomic

N.I. Kargin // Scientific Visualization. 2015. Vol. 7. P. 30–37.

7.O.K. Rice, H.C. Ramsperger. Theories of unimolecular gas reactions at low pressures // Journal of American Chemical Society. 1927. Vol. 49. P. 1617–1629.

8.P. Attard. Stochastic molecular dynamics: A combined Monte Carlo and molecular dynamics technique for isothermal simulations // Journal of Chemical Physics. 2002. Vol. 116. P. 9616–9619.

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