ИСТИНА

Computational techniques for modeling of polymer nanocomposites have been emerged rapidly in recent years as an important complement to experiment. The present work is aimed at development of a new methodology for the construction of atomistic models of polymer matrices with different nanofillers. There are four separate stages involved in our methodology, which is based on multiscale simulations. We begin with mapping of initial monomers and nanoparticles of a filler onto coarse-grained representation using a neural-gas algorithm. Then we construct a coarse-grained model of a polymer matrix by using the dissipative particle dynamics (DPD) method to simulate chemical reaction between monomers. The reaction proceeds toward equilibrium until the desired degree of conversion is reached. At the next stage, the reverse mapping of the obtained coarse-grained model is used to restore a fully atomistic representation of a system under study. Finally, the constructed atomistic models are equilibrated through standard molecular dynamics (MD) technique. To check the developed approach, we have evaluated the density, the glass transition temperature, and the thermal expansion coefficient of the cross-linked polymer matrices with and without nanoparticles, depending on the effective conversion and temperature.