Issue 7, 2024

Machine learning methods for liquid crystal research: phases, textures, defects and physical properties

Abstract

Liquid crystal materials, with their unique properties and diverse applications, have long captured the attention of researchers and industries alike. From liquid crystal displays and electro-optical devices to advanced sensors and emerging technologies, the study and application of liquid crystals continue to be of paramount importance in the fields of materials science, chemistry and physics. With the ever-increasing complexity and diversity of liquid crystal materials, researchers face new challenges in understanding their behaviors, properties, and potential applications. On the other hand, machine learning, a rapidly evolving interdisciplinary field at the intersection of computer science and data analysis, has already become a powerful tool for unraveling implicit correlations and predicting new properties of a wide variety of physical and chemical systems and structures. Here we aim to consider how machine learning methods are suitable for solving fundamental problems in the field of liquid crystals and what are the advantages of this artificial intelligence based approach.

Graphical abstract: Machine learning methods for liquid crystal research: phases, textures, defects and physical properties

Article information

Article type
Review Article
Submitted
03 dez 2023
Accepted
22 jan 2024
First published
24 jan 2024

Soft Matter, 2024,20, 1380-1391

Machine learning methods for liquid crystal research: phases, textures, defects and physical properties

A. Piven, D. Darmoroz, E. Skorb and T. Orlova, Soft Matter, 2024, 20, 1380 DOI: 10.1039/D3SM01634J

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