Machine learning methods for liquid crystal research: phases, textures, defects and physical properties
A Piven, D Darmoroz, E Skorb, T Orlova - Soft Matter, 2024 - pubs.rsc.org
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 …
captured the attention of researchers and industries alike. From liquid crystal displays and …
The use of artificial intelligence in liquid crystal applications: A review
S Chattha, PK Chan, SR Upreti - The Canadian Journal of …, 2024 - Wiley Online Library
Recent advancements in artificial intelligence (AI) have significantly influenced scientific
discovery and analysis, including liquid crystals. This paper reviews the use of AI in …
discovery and analysis, including liquid crystals. This paper reviews the use of AI in …
Machine learning classification of polar sub-phases in liquid crystal MHPOBC
R Betts, I Dierking - Soft Matter, 2023 - pubs.rsc.org
Experimental polarising microscopy texture images of the fluid smectic phases and sub-
phases of the classic liquid crystal MHPOBC were classified as paraelectric (SmA*) …
phases of the classic liquid crystal MHPOBC were classified as paraelectric (SmA*) …
Nonlocal interactions between vegetation induce spatial patterning
Vegetation pattern provides useful signals for vegetation protection and can be identified as
an early warning of desertification. In some arid or semi-arid regions, vegetation absorbs …
an early warning of desertification. In some arid or semi-arid regions, vegetation absorbs …
Testing different supervised machine learning architectures for the classification of liquid crystals
I Dierking, J Dominguez, J Harbon, J Heaton - Liquid Crystals, 2023 - Taylor & Francis
Different convolutional neural network (CNN) and inception network architectures were
trained for the classification of isotropic, nematic, cholesteric and smectic liquid crystal phase …
trained for the classification of isotropic, nematic, cholesteric and smectic liquid crystal phase …
Distinguishing the Focal-Conic Fan Texture of Smectic A from the Focal-Conic Fan Texture of Smectic B
N Osiecka-Drewniak, Z Galewski… - Crystals, 2023 - mdpi.com
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 …
phase A (SmA) and the crystalline smectic B phase (CrB). Most often, characteristic …
Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms
This study aims to examine the influence of the co-doped semiconductor nanostructure (Al-
Cu): ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal …
Cu): ZnO on the electro-optical properties of the E7 coded pure nematic liquid crystal …
[HTML][HTML] Complexity measurements for the thermal convection in a viscoelastic fluid saturated porous medium
Measuring complexity statistical indicators is a key method to analyze and characterize
dynamical systems. In this work, we perform a comparative analysis among the López-Ruiz …
dynamical systems. In this work, we perform a comparative analysis among the López-Ruiz …
Probing modulated liquid crystal media with dielectric spectroscopy
We use impedance spectroscopy to probe modulated liquid crystals. Chiral nematic samples
are characterized and fabricated with a fixed amount of chiral dopant and Smectic-A in …
are characterized and fabricated with a fixed amount of chiral dopant and Smectic-A in …
Machine learning for soft and liquid molecular materials
This review discusses three types of soft matter and liquid molecular materials, namely
hydrogels, liquid crystals and gas bubbles in liquids, which are explored with an emergent …
hydrogels, liquid crystals and gas bubbles in liquids, which are explored with an emergent …