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

Abstract

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.

11 two-column pages, 7 figures; accepted for publication in Physical Review E

References in corpus (14)

• scikit-image: Image processing in Python
• Machine learning phases of matter
• A Statistical Measure of Complexity
• Identifying structural flow defects in disordered solids using machine learning methods
• Discovering Phases, Phase Transitions and Crossovers through Unsupervised Machine Learning: A critical examination
• Orientational order of motile defects in active nematics
• History of art paintings through the lens of entropy and complexity
• Machine learning out-of-equilibrium phases of matter
• Machine learning for many-body physics: The case of the Anderson impurity model
• Complexity-Entropy Causality Plane as a Complexity Measure for Two-dimensional Patterns
• Complexity-entropy causality plane: a useful approach for distinguishing songs
• Characterizing Time Series via Complexity-Entropy Curves
• Identifying polymer states by machine learning
• Discriminating image textures with the multiscale two-dimensional complexity-entropy causality plane

Cited by in corpus (12)

• ordpy: A Python package for data analysis with permutation entropy and ordinal network methods
• Learning physical properties of liquid crystals with deep convolutional neural networks
• Collective dynamics of stock market efficiency
• Determining liquid crystal properties with ordinal networks and machine learning
• Mapping images into ordinal networks
• Two new parameters for the ordinal analysis of images
• Characterizing unstructured data with the nearest neighbor permutation entropy
• Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials
• Clustering free-falling paper motion with complexity and entropy
• Nearest neighbor permutation entropy detects phase transitions in complex high-pressure systems
• Nonequilibrium thermodynamics of self-supervised learning
• Interpretable liquid crystal phase classification via two-by-two ordinal patterns