Machine learning is making a major impact in materials research. I review current progress
across a selection of areas of ubiquitous soft matter. When applied to particle tracking …

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 …

The performance of a convolutional neural network (CNN) heavily depends on its
hyperparameters. However, finding a suitable hyperparameters configuration is difficult …

Experimental polarising microscopy texture images of the fluid smectic phases and sub-
phases of the classic liquid crystal MHPOBC were classified as paraelectric (SmA*) …

Liquid crystals (LCs) are key functional materials that are broadly adopted in various fields
due to their stimuli‐responsiveness. Recently, LCs with hierarchical architectures have …

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 …

We investigate the application of convolutional neural networks (CNNs) to the classification
of liquid crystal phases from images of their experimental textures. Three CNN classifier …

Different convolutional neural network (CNN) and inception network architectures were
trained for the classification of isotropic, nematic, cholesteric and smectic liquid crystal phase …

This publication presents methods of distinguishing the focal texture of the conical smectic
phase A (SmA) and the crystalline smectic B phase (CrB). Most often, characteristic …

Deep Learning techniques such as supervised learning with convolutional neural networks
and inception models were applied to phase transitions of liquid crystals to identify transition …