Lily-like twist distribution in toroidal nematics

TL;DR

This paper investigates the twist distribution in toroidal nematic liquid crystal droplets, revealing a surprising lily-like stable configuration and introducing a novel deep-learning optimization method for complex elastic free-energy problems.

Contribution

It demonstrates that the stable twist distribution in toroidal nematics is lily-like, contrary to previous conjectures, and develops a deep-learning approach for non-linear elastic energy optimization.

Findings

Stable twist distribution is lily-like, not fennel-like.

Maximum twist deflection occurs within the torus, not at the boundary.

Deep-learning optimization effectively handles complex non-linear problems.

Abstract

Toroidal nematics are droplets of nematic liquid crystals in the form of a circular torus. When the nematic director is subject to planar degenerate boundary conditions, the bend-only director field with vector lines along the parallels of all internal torodial shells is an equilibrium solution for all values of the elastic constants. Local stability analyses have shown that an instability is expected to occur for sufficiently small values of the twist elastic constant. It is natural to conjecture that in this regime the global equilibrium would be characterized by a maximum twist deflection on the boundary of the torus, with a twist distribution over the torus' cross-section represented by a fennel-like surface. We prove that surprisingly the stable twist distribution is instead represented by a lily-like surface. Thus the overall maximum twist deflection falls well within the torus. To cope with the complexity of the elastic free-energy functional in the fully non-linear setting, we developed an ad hoc deep-learning optimization method, which here is also duly validated and documented for it promises to be applicable to other similar problems, equally intractable analytically.