Micellization Studied by GPU-Accelerated Coarse-Grained Molecular Dynamics.

ABSTRACT

The computational design of advanced materials based on surfactant self-assembly without ever stepping foot in the laboratory is an important goal, but there are significant barriers to this approach, because of the limited spatial and temporal scales accessible by computer simulations. In this paper, we report our work to bridge the gap between laboratory and computational time scales by implementing the coarse-grained (CG) force field previously reported by Shinoda et al. [Shinoda, W.; DeVane, R.; Klein, M. L. Mol. Simul. 2007, 33, 27-36] into the HOOMD-Blue graphical processing unit (GPU)-accelerated molecular dynamics (MD) software package previously reported by Anderson et al. [Anderson, J. A.; Lorenz, C. D.; Travesset, A. J. Comput. Phys. 2008, 227, 5342-5359]. For a system of 25 750 particles, this implementation provides performance on a single GPU, which is superior to that of a widely used parallel MD simulation code running on an optimally sized CPU-based cluster. Using our GPU setup, we have collected 0.6 ms of MD trajectory data for aqueous solutions of 7 different nonionic polyethylene glycol (PEG) surfactants, with most of the systems studied representing ∼1 000 000 atoms. From this data, we calculated various properties as a function of the length of the hydrophobic tails and PEG head groups. Specifically, we determined critical micelle concentrations (CMCs), which are in good agreement with experimental data, and characterized the size and shape of micelles. However, even with the microsecond trajectories employed in this study, we observed that the micelles composed of relatively hydrophobic surfactants are continuing to grow at the end of our simulations. This suggests that the final micelle size distributions of these systems are strongly dependent on initial conditions and that either longer simulations or advanced sampling techniques are needed to properly sample their equilibrium distributions. Nonetheless, the combination of coarse-grained modeling and GPU acceleration marks a significant step toward the computational prediction of the thermodynamic properties of slowly evolving surfactant systems.