When examined with polarized optical
microscopy (POM),
liquid crystals
display interference colors and complex patterns that depend on the
material’s microscopic orientation. That orientation can be
manipulated by the application of external fields, a feature that
provides the basis for applications in optical display and sensing
technologies. The color patterns themselves have high information
content. Traditionally, however, calculations of the optical appearance
of liquid crystals have been performed by assuming that a single-wavelength
light source is employed and reported on a monochromatic scale. In
this work, the original Jones matrix method is extended to calculate
the colored images that arise when a liquid crystal is exposed to
a multiwavelength source. By accounting for the material properties,
including the local orientation, the visible light spectrum, and the
CIE (International Commission on Illumination) color matching functions,
we demonstrate that the proposed approach produces colored POM images
that are in quantitative agreement with experimental data. Results
are presented for a variety of systems, including radial, bipolar,
and cholesteric droplets, where results of simulations are compared
with experimental images. The effects of the droplet size, topological
defect structure, and droplet orientation are examined systematically.
The technique introduced here generates images that can be directly
compared to experiments, thereby facilitating machine learning efforts
aimed at interpreting LC microscopy images and paving the way for
the inverse design of materials capable of producing specific internal
microstructures in response to external stimuli.