Promising Route for the Development of a Computational Framework for Self-Assembly and Phase Behavior Prediction of...

Surfactants are amphiphilic molecules with multiple uses and industrial applications as detergents, wetting agents, emulsifiers, and so forth. They can be divided into three main categories: nonionic, ionic, and zwitterionic. The development of a universal computational framework able to predict key properties such as their critical micelle concentration (cmc) and the size of the micelles they form and to ultimately extract phase diagrams for their aqueous solutions, possibly in the presence of salts and oils, using their chemical constitution as input, would provide valuable information for the design and the production of these materials. In this work, we focus on ionic surfactants and investigate a possible route toward the development of such a framework based on coarse-grained simulations using the MARTINI forcefield in two versions: its implicit solvent version, called Dry MARTINI, and its explicit solvent version, called Wet MARTINI. The surfactants considered in our efforts are the anionic sodium dodecyl sulfate (SDS) and the three cationic cetyl, dodecyl, and octyl trimethyl ammonium bromide (CTAB, DTAB, and OTAB, respectively). First, we choose their mapping onto coarse-grained MARTINI beads. Next, we estimate their cmc's, their peak aggregation numbers, N_agg, and in the case of SDS, its small angle neutron scattering pattern at low concentrations, applying the Dry MARTINI forcefield. With a single modification to the Lennard-Jones energy parameter between hydrophobic beads and invoking Ewald summation with a physically meaningful dielectric constant for electrostatic interactions, our estimates are in very good agreement with experimental results. Furthermore, we predict the phase behavior of SDS/water and CTAB/water binary solutions using Wet MARTINI and find semiquantitative agreement with experimental phase diagrams. We conclude that the MARTINI forcefield, with careful treatment of electrostatic interactions and appropriate modification of parameters for some key functional groups, can be a powerful ally in the quest for a universal computational framework for the design of new surfactants with improved properties.