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 …

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 …

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*) …

Nonlocal interactions between vegetation induce spatial patterning

J Liang, C Liu, GQ Sun, L Li, L Zhang, M Hou… - Applied Mathematics …, 2022 - Elsevier
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 …

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 …

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 …

Minimization of the threshold voltage parameter of the co-doped ZnO doped liquid crystals by machine learning algorithms

G Önsal, O Uğurlu, ÜH Kaynar, D Türsel Eliiyi - Scientific Reports, 2023 - nature.com
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 …

[HTML][HTML] Complexity measurements for the thermal convection in a viscoelastic fluid saturated porous medium

LM Pérez, JA Vélez, MN Mahmud, RM Corona… - Results in Physics, 2023 - Elsevier
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 …

Probing modulated liquid crystal media with dielectric spectroscopy

MP Rosseto, RRR de Almeida, EK Lenzi… - Journal of Molecular …, 2023 - Elsevier
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 …

Machine learning for soft and liquid molecular materials

T Orlova, A Piven, D Darmoroz, T Aliev, TMTA Razik… - Digital …, 2023 - pubs.rsc.org
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 …