An unconstrained DFT approach to microphase formation and application to binary Gaussian mixtures

Area of Science:

• Soft matter physics
• Computational physics
• Materials science

Background:

• Particle systems with repulsive, bounded potentials can form microphases.
• Density-functional theory (DFT) is a powerful tool for studying such systems.
• Previous studies suggested microphase formation in binary Gaussian particle mixtures, but lacked detailed investigation.

Purpose of the Study:

• To investigate microphase formation in a binary mixture of Gaussian particles using DFT.
• To construct a detailed phase diagram for this system.
• To explore novel phases and compare cluster formation with other models.

Main Methods:

• Employed density-functional theory (DFT) with a mean-field-like free energy functional.
• Did not assume a specific functional form for the density profile, allowing for periodic configurations.
• Minimized free energy with respect to density values and lattice vectors.
• Validated the method using a one-component generalized exponential model (GEM) fluid.

Main Results:

• Generated a detailed phase diagram for the binary Gaussian particle mixture.
• Observed cluster, tubular, and bicontinuous microphases, analogous to those in block copolymers and surfactant mixtures.
• Discovered two non-cubic phases featuring helical structures of alternating chirality.
• Bicontinuous phases were found to occupy a significant portion of the phase diagram.

Conclusions:

• The DFT approach accurately predicts microphase formation in soft particle systems.
• The binary Gaussian particle mixture exhibits rich phase behavior, including complex helical structures.
• The findings provide new insights into the self-assembly of soft matter and potential applications.