(527d) Multiscale Molecular Modeling of Polymer/Silica Nanocomposites

One of the major goals of computational material science is the rapid and accurate prediction of properties of new materials. In order to develop new materials and compositions with designed new properties, it is essential that these properties can be predicted before preparation, processing, and characterization. Despite the tremendous advances made in the modeling of structural, thermal, mechanical and transport properties of materials at the macroscopic level (finite element (FE) analysis of complicated structures), there remains a tremendous uncertainty about how to predict many critical properties related to performance. The fundamental problem here is that these properties depend on the structure that the material exhibits at a length scale ranging from few to some dozens of nanometers, and this structure depends strongly on the interactions at atomistic scale. To substantially advance the ability to design useful high performance materials, it is then essential that we insert the chemistry into the mesoscopic (MS) modeling. Currently, atomistic level simulations such as molecular dynamics (MD) or Monte Carlo (MC) techniques allows to predict the structure and properties for systems of considerably large number of atoms and time scales of the order of microseconds. Although this can lead to many relevant results in material design, many critical issues in materials design still require time and length scales far too large for practical MD/MC simulations. Given these concepts, it is than necessary to carry out calculations for realistic time scales fast enough to be useful in design. This requires developing techniques useful to design engineers, by incorporating the methods and results of the lower scales (e.g., MD) to mesoscale simulations. One of the most reliable and used method for mesoscale simulation is the dissipative particle dynamics (DPD) in which the equation of motion is applied to agglomerates of matter (beads) that are subject to a soft potential. One of the main issue in applying DPD to real systems is the estimation of the parameters to be inserted in the soft potential. In particular the interaction parameter plays a relevant role in the morphology of the system. In this work, we developed a multiscale modeling procedure to predict the morphology at nanoscale and the consequent macroscopic properties for nanocomposites basd on polymers and silica-grafted nanoparticles. The recipe is based on the following steps:(i) estimation of DPD interactions parameters via calculation of interaction energies by atomistic molecular dynamics simulations; (ii) their mapping onto mesoscale energies; (iii) DPD simulations for morphology determination, and (iv) estimation of macroscopic thermal and mechanical properties by finite element calculations. Systems of direct industrial interest will be considered, and comparison with experiments will be discussed.