Canonical Simulation Methodology to Extract Phase Boundaries of Liquid Crystalline Polymer Mixtures

TL;DR

This paper introduces a multi-scale simulation approach combining coarse-grained molecular dynamics and mean-field theory to predict phase boundaries in liquid crystalline polymer mixtures, capturing nematic and smectic phases.

Contribution

It presents a novel methodology integrating CGMD and mean-field models to accurately determine phase diagrams of complex polymer mixtures with orientational order.

Findings

Successfully predicts nematic and smectic phase boundaries.

Semi-quantitative agreement between simulations and mean-field phase diagram.

Applicable to synthetic and biological macromolecule mixtures.

Abstract

We report a novel multi-scale simulation methodology to quantitatively predict the thermodynamic behaviour of polymer mixtures, that exhibit phases with broken orientational symmetry. Our system consists of a binary mixture of oligomers and rod-like mesogens. Using coarse-grained molecular dynamics (CGMD) simulations we infer the topology of the temperature-dependent free energy landscape from the probability distributions of excess volume fraction of the components. The mixture exhibits nematic and smectic phases as a function of two temperature scales, the nematic-isotropic temperature $T_{NI}$ and the $T_c$, the transition that governs the polymer demixing. Using a mean-field free energy of polymer-dispersed liquid crystals (PDLCs), with suitably chosen parameter values, we construct a mean-field phase diagram that semi-quantitatively match those obtained from CGMD simulations. Our results are applicable to mixtures of synthetic and biological macromolecules that undergo phase separation and are orientable, thereby giving rise to the liquid crystalline phases.