Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information.

Topological defects in liquid crystals (LCs) dominate molecular alignment/motion in many cases. Here, the neural network (NN) function has been introduced to predict the LC orientation condition (orientation angle and order parameter) at local positions around topological defects from the phase/polarization microscopic color images. The NN function was trained in advance by using the color information of an LC in a planar alignment cell for different orientation angles and temperatures.

Molecular Orientation Change Nearby Topological Defects Observed by Photo-Induced Polarization/Phase Microscopy.

Topological defects in liquid crystals (LCs) have been intensively studied and intentionally generated in an organized way recently because they could control the alignment and motion of LCs. We studied how the topological defects could change the molecular orientation/alignment from the observation of photo-induced orientation change of a photo-responsive LC. The photo-induced dynamics was observed by an LED-induced time-resolved polarization/phase microscopy with white light illumination.