Theoretically informed Monte Carlo simulation of liquid crystals by sampling of alignment-tensor fields

Area of Science:

• Computational Physics
• Materials Science
• Soft Matter Physics

Background:

• Studying liquid crystal behavior is crucial for materials science and device applications.
• Traditional free energy minimization techniques can struggle with complex metastable states in liquid crystals.

Purpose of the Study:

• To develop and validate a theoretically informed coarse-grained Monte Carlo method for liquid crystal simulations.
• To demonstrate the method's ability to identify lower free energy states, particularly in systems with multiple metastable morphologies.

Main Methods:

• Utilized a coarse-grained Monte Carlo approach informed by Landau-de Gennes theory.
• Approximated the alignment field and gradients using finite differences.
• Employed stochastic sampling for free energy minimization.

Main Results:

• The proposed Monte Carlo method successfully identified lower free energy states in nematic and chiral liquid crystal systems.
• Demonstrated superior performance over traditional methods in finding states missed by conventional approaches.
• Successfully identified states from random initial configurations, eliminating the need for specific initial guesses.

Conclusions:

• The developed Monte Carlo method provides a robust and efficient tool for studying liquid crystals, especially those with complex free energy landscapes.
• This simulation technique simplifies the study of liquid crystals by removing the requirement for expert-initialized configurations.