Determining liquid crystal properties with ordinal networks and machine learning

TL;DR

This study introduces a novel machine learning approach using ordinal networks to analyze optical textures of liquid crystals, enabling accurate classification of phase transitions, doping effects, and temperature predictions from image data.

Contribution

The paper presents a new application of ordinal networks combined with simple machine learning models for analyzing liquid crystal textures, demonstrating high accuracy and scalability.

Findings

Successfully classified mesophase transitions.

Distinguished doping concentrations in liquid crystals.

Predicted sample temperatures with high precision.

Abstract

Machine learning methods are becoming increasingly important for the development of materials science. In spite of this, the use of image analysis in the development of these systems is still recent and underexplored, especially in materials often studied via optical imaging techniques such as liquid crystals. Here we apply the recently proposed method of ordinal networks to map optical textures obtained from experimental samples of liquid crystals into complex networks and use this representation jointly with a simple statistical learning algorithm to investigate different physical properties of these materials. Our research demonstrates that ordinal networks formed by only 24 nodes encode crucial information about liquid crystal properties, thus allowing us to train simple machine learning models capable of identifying and classifying mesophase transitions, distinguishing among different doping concentrations used to induce chiral mesophases, and predicting sample temperatures with outstanding accuracy. The precision and scalability of our approach indicate it can be used to probe properties of different materials in situations involving large-scale datasets or real-time monitoring systems.