Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods
This work addresses the challenge of estimating physical properties from liquid crystal textures for materials science researchers, representing an incremental advancement by applying existing methods to a new domain.
The authors tackled the problem of extracting physical properties of liquid crystals directly from texture images by combining physics-inspired image quantifiers with machine learning, achieving significant precision in predicting properties like average order parameter, temperature, and cholesteric pitch length.
Imaging techniques are essential tools for inquiring a number of properties from different materials. Liquid crystals are often investigated via optical and image processing methods. In spite of that, considerably less attention has been paid to the problem of extracting physical properties of liquid crystals directly from textures images of these materials. Here we present an approach that combines two physics-inspired image quantifiers (permutation entropy and statistical complexity) with machine learning techniques for extracting physical properties of nematic and cholesteric liquid crystals directly from their textures images. We demonstrate the usefulness and accuracy of our approach in a series of applications involving simulated and experimental textures, in which physical properties of these materials (namely: average order parameter, sample temperature, and cholesteric pitch length) are predicted with significant precision. Finally, we believe our approach can be useful in more complex liquid crystal experiments as well as for probing physical properties of other materials that are investigated via imaging techniques.