Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods

Optical imaging techniques are important tools extensively used for probing a number of materials properties [1]. These imaging techniques are non-destructive and particularly convenient for dealing with biological and other complex materials [2]. Liquid crystals are among these materials widely studied via optical and image processing methods [3]. This occurs because liquid crystals are birefringent materials, and as such, simple polarized optical microscope imaging already access some of their important properties, including birefringence and sample thickness [4]. Moreover, this technique estimates the local ordering properties (for instance, the director distribution) across a sample when coupled with variable retarders and different algorithms for fast and sensitive measurements [5]. This approach is known as LC-PolScope [6] and has been used for fine imaging of defect cores in lyotropic liquid crystals [7] and can describe the orientational order of active nematics [8]. Despite the extensive use of optical imaging approaches in the study of liquid crystals [9-12], much less attention has been paid to the problem of extracting physical parameters directly from images of these materials. This is an important issue since several physical parameters of liquid crystals are only obtained by adjusting theoretical models to cumbersome and time demanding experimental results. Examples include the microscopic order parameter, from which several other parameters characterizing the nematic phase are dependent [3], and the pitch length of cholesteric liquid crystals.