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.