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

Piven, Anastasiia; Darmoroz, Darina; Skorb, Ekaterina; Orlova, Tetiana

doi:10.1039/d3sm01634j

Soft Matter

2024

DOI: 10.1039/d3sm01634j

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

Anastasiia Piven,

Darina Darmoroz,

Ekaterina Skorb

et al.

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...

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2024

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Cited by 5 publications

References 109 publications

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Order‐Parameter‐Free Analysis of Soft Matter: Applications of Machine Learning via Image Recognition

Terao,

Kondo

2024

Annalen der Physik

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Various characteristic structures, with no long‐range spatial order, have often been observed in studies on the structural formation of soft materials. The order parameters, used to date, are not promising for computer detection of these types of structures. In this previous study, it is shown that machine‐learning analysis using convolutional neural networks is very effective for the structural formation of spherical colloidal particles. This method is applied to non‐spherical inverse patchy colloids and demonstrated that this order‐parameter‐free analysis method is effective for non‐spherical soft matter, which often exhibits complex structures. A recent development in the structural formation of colloidal particle systems corresponds to the problem of monolayers of core‐corona particle systems that exhibit a variety of structures. Monte Carlo simulations are performed for core‐corona particles, confined between parallel plates, to clarify the conditions for the appearance of the bilayer and its in‐plane structure formation. Parameter‐free analysis is performed using image‐based machine learning. The bilayer formation of the Jagla fluids is performed, and the phase diagram is clarified.

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Order‐Parameter‐Free Analysis of Soft Matter: Applications of Machine Learning via Image Recognition

Terao,

Kondo

2024

Annalen der Physik

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Shape memory liquid crystalline polymers: Stimuli‐responsiveness, advanced technologies, and key applications

Prathumrat,

Nikzad,

Arablouei

et al. 2024

Polymers for Advanced Techs

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Liquid crystalline polymers (LCPs) represent a distinct class of materials that have garnered significant interest for their utilisation in diverse industrial and engineering applications. A prominent attribute of LCPs is their stimuli‐responsiveness. These materials can undergo deformation and subsequently recover their original shapes when subjected to external stimuli such as heat, light, and electromagnetic fields. The molecular structure of LCPs consists of mesogens and flexible tails, mirroring the fundamental molecular mechanism found in shape memory polymers. This characteristic positions LCPs as promising materials for shape memory applications. This article provides a comprehensive review of LCPs, focusing on their various forms of stimuli‐responsiveness. In addition, it delves into the application of additive manufacturing and machine learning technologies in the context of shape memory LCPs. Finally, the article concludes by exploring the critical applications of LCPs as shape memory materials.

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Induced nematic phase with improved thermal characteristics for new maleic anhydride derivatives

Al-Humaidi,

Ahmed,

Albedair

et al. 2024

Molecular Crystals and Liquid Crystals

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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...

Cited by 5 publications

References 109 publications

Order‐Parameter‐Free Analysis of Soft Matter: Applications of Machine Learning via Image Recognition

Order‐Parameter‐Free Analysis of Soft Matter: Applications of Machine Learning via Image Recognition

Shape memory liquid crystalline polymers: Stimuli‐responsiveness, advanced technologies, and key applications

Induced nematic phase with improved thermal characteristics for new maleic anhydride derivatives

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