Abstract

Atomistic based simulations such as molecular mechanics (MM), molecular dynamics (MD), and Monte Carlo-based methods (MC) have come into wide use for material design. Using these atomistic simulation tools, we can analyze molecular structure on the scale of 0.1–10 nm. However, difficulty arises concerning limitations of the time-and length-scale involved in the simulation. Although a possible molecular structure can be simulated by the atom-based simulations, its is less realistic to predict the mesoscopic structure defined on the scale of 100–1000 nm, for example the morphology of polymer blends and composites, which often dominates actual material properties. For the morphology on these scales, mesoscopic simulations such as the dynamic mean field density functional theory (Mesodyn) and dissipative particle dynamics (DPD) are available as alternatives to atomistic simulations. It is therefore inevitable to adopt a mesoscopic simulation technique and bridge the gap between atomistic and mesoscopic simulations for an effective material design. Furthermore, it is possible to transfer the simulated mesoscopic structure to finite elements modeling tools (FEM) for calculating macroscopic properties for the systems of interest. In this presentation, a hierarchical procedure for bridging the gap between atomistic and macroscopic (FEM) modeling passing through mesoscopic simulations will be presented and discussed. The concept of multiscale (or many scale) modeling will be outlined, and examples of applications of single scale and multiscale procedures for nanostructured systems of industrial interest will be presented. In particular the following industrial applications will be considered: (i) polymerorganoclay nanocomposites for the estimation of the binding energy and basal spacing of a montmorillonite—polymer—surface modifier system; (ii) mesoscale simulation for diblock copolymers with dispersion of nanoparticels; (iii) polymer—carbon nanotubes system and (iv) applications of multiscale modeling for process systems engineering.

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