Predicting surfactant phase behavior with a molecularly informed field theory.

Area of Science:

• Computational Chemistry
• Materials Science
• Soft Matter Physics

Background:

• Modeling surfactant self-assembly requires chemically accurate interactions and efficient equilibration.
• Conventional molecular dynamics faces challenges in simulating long timescales for self-assembled systems.

Purpose of the Study:

• To develop a multiscale simulation workflow combining all-atom (AA) and coarse-grained (CG) methods for surfactant self-assembly.
• To enable rigorous equilibration and study of complex self-assembled structures using field-theoretic simulations.

Main Methods:

• Utilizing relative entropy minimization to transfer chemical detail from AA to CG models.
• Employing field-theoretic simulations for CG models, allowing free energy minimization for equilibration.
• Calibrating an AA force field against interfacial tension experiments for chemical fidelity.

Main Results:

• The multiscale workflow successfully models surfactant behavior, exemplified by sodium dodecylsulfate.
• Generated CG models accurately reproduce AA simulations and experimental measurements, including salt effects.
• The approach enables the study of complex 3D phases such as double or alternating gyroids.

Conclusions:

• The proposed multiscale simulation approach effectively overcomes challenges in modeling surfactant self-assembly.
• This method provides a robust framework for studying properties difficult to access with particle-based simulations alone.
• The workflow facilitates the investigation of complex self-assembled structures and their responses to environmental factors.