(201b) Lipid Self Assembly by Statistical Temperature Monte Carlo and Molecular Dynamics

The recently developed statistical temperature Monte Carlo (STMC) and statistical temperature molecular dynamics (STMD) [1] methods have been applied to study the self-assembly process of amphiphilic molecules (bilayer and micelle formation). STMC and STMD are extensions of the Wang-Landau (WL) method [2], from which a flat energy distribution is also attained by updating the statistical temperature rather than the density of states (as is done in the WL method). However, this updating of the intensive quantity temperature provides a continuum description for the complete density of states without imposing limitations on the size of the energy bins.

As a test of the ability of the STMC and STMD methods to study self assembly, two systems have been studied: a lattice based implicit solvent 3-segment model and an off-lattice explicit-solvent 3-segment model. In the lattice model, varying sizes of energy bins are simulated with the STMC method. A bilayer structure is found to form at low temperatures, with phase transitions to clusters as temperature increases.  The results are in good agreement with phase diagrams calculated by traditional Metropolis and the original WL methods. Additionally by allowing the use of larger energy bins, the simulation converges faster by the STMC method than by the original WL method.

For the off-lattice model, a systematic study of the thermodynamic properties will be presented, as well as the effect of the strength of molecular interactions (hydrophobic and hydrophilic) on the structures formed discussed. The STMD method makes well use of the WL sampling idea with molecular dynamics move, which is applicable to search global minima for complex systems, where effective STMC moves are not practical.

1.  J. Kim, J. E. Straub and T. Keyes, “Statistical-Temperature Monte Carlo and Molecular Dynamics Algorithms,” Phys. Rev. Lett. 97 (2006) 050601

2.  F. Wang and D. P. Landau, “Efficient, Multiple-Range Random Walk Algorithm to Calculate the Density of States,” Phys. Rev. Lett. 86 (2001) 2050–2053.