Coarse-grained force field for simulating polymer-tethered silsesquioxane self-assembly in solution.
A coarse-grained model has been developed for simulating the self-assembly of nonyl-tethered polyhedral oligomeric silsesquioxane (POSS) nanoparticles in solution. A mapping scheme for groups of atoms in the atomistic molecule onto beads in the coarse-grained model was established. The coarse-grained force field consists of solvent-mediated effective interaction potentials that were derived via a structural-based coarse-graining numerical iteration scheme. The force field was obtained from initial guesses that were refined through two different iteration algorithms. The coarse-graining scheme was validated by comparing the aggregation of POSS molecules observed in simulations of the coarse-grained model to that observed in all-atom simulations containing explicit solvent. At 300 K the effective coarse-grained potentials obtained from different initial guesses are comparable to each other. At 400 K the differences between the force fields obtained from different initial guesses, although small, are noticeable. The use of a different iteration algorithm employing identical initial guesses resulted in the same overall effective potentials for bare cube corner bead sites. In both the coarse-grained and all-atom simulations, small aggregates of POSS molecules were observed with similar local packings of the silsesquioxane cages and tether conformations. The coarse-grained model afforded a savings in computing time of roughly two orders of magnitude. Further comparisons were made between the coarse-grained monotethered POSS model developed here and a minimal model developed in earlier work. The results suggest that the interactions between POSS cages are long ranged and are captured by the coarse-grained model developed here. The minimal model is suitable for capturing the local intermolecular packing of POSS cubes at short separation distances.
Chan
Elaine R
ER
Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109-2136, USA. elaine.chan@nist.gov
Striolo
Alberto
A
McCabe
Clare
C
Cummings
Peter T
PT
Glotzer
Sharon C
SC