Moleculary Informed Field-Theoretic Models for Constructing the Phase Diagrams of Surfactant Formulations

Surfactants are amphiphilic molecules that can self-assemble in aqueous solution into a variety of supramolecular arrays, such as spherical micelles or periodic liquid crystal-like phases. The morphologies of these self-assembled mesostructures are major determinants for material functionality and are controllable via a multivariate design space (temperature, composition, chemistry, etc.). However, experimentally constructing each surfactant’s phase diagram one by one can be extremely exhaustive. Thus, improved methods for predictive computational modeling are attractive for efficiently sweeping the expansive design space. Modern computational chemistry techniques, such as all-atom molecular dynamics and coarse-grained methods, are limited either by time and length scale disparities or predictive power in studying surfactant self-assembly. To overcome these limitations, we employ a novel multiscale methodology that utilizes small-scale atomistic molecular dynamics simulations to parameterize statistical field theory models via bottom-up coarse-graining. The molecularly informed field theory can then be sampled via mean-field theory (SCFT) calculations to efficiently determine the globally stable surfactant mesostructure under varying conditions. In this project, we will demonstrate how we apply this multiscale methodology to determine the equilibrium phase behavior of anionic surfactant SDS under isothermal conditions and to further interpolate a full temperature versus composition binary phase diagram for cationic surfactant CTAB. The correct qualitative ordering of phases and, in some cases, quantitative phase transition points are predicted for both SDS and CTAB, which show our methodology may be the closest thing to “printing out” a phase diagram de novo. In the future, this multiscale methodology has potential to be integrated into a high-throughput screening workflow to inexpensively compute phase diagrams of novel surfactant chemistries.