Prediction of cationic surfactant phase diagrams via molecularly informed field theory

Surfactant self-assembly in soft matter formulations spans a complex, multivariate design space, motivating the development of efficient and predictive computational modeling approaches to aid formulation design. However, conventional molecular simulation techniques, including all-atom molecular dynamics and coarse-grained methods, are limited by either accessible time and length scales or predictive accuracy in studying surfactant self-assembly. To address these challenges, we employ a multiscale methodology that uses small-scale all-atom simulations to parameterize statistical field-theoretic models via bottom-up coarse-graining, eliminating the need for experimental input. The resulting molecularly informed field theory is then sampled using self-consistent field theory calculations to efficiently predict self-assembly and phase behavior. We demonstrate this approach by constructing binary phase diagrams for cationic alkyl quaternary ammonium surfactants (C₁₆TAB, C₁₆TAC, and C₁₀TAB) in water across a range of temperatures, compositions, and salt concentrations. The model successfully captures all experimentally observed surfactant mesophases and reproduces the majority of phase transition orderings de novo. In addition, we show this approach provides a unified framework for predicting equilibrium properties such as mesostructure domain sizes, micelle aggregation numbers, and critical micelle concentrations with qualitative agreement to experimentally observed trends. This multiscale methodology has the potential to be integrated into high-throughput screening workflows for efficient prediction of phase diagrams in novel surfactant formulations.