Crystal-like topological defect arrays in nematic liquid crystal

Tsung, Jieh Wen; Wang, Ya Zi; Yao, Sheng; Chao, Shih Yu

doi:10.1063/5.0064303

Cited by 6 publications

(4 citation statements)

References 41 publications

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“…Topological descriptors are capable of extracting information from multidimensional data and are tolerant to noise; in addition, topological descriptors can provide natural connections with physical phenomena that drives behavior. For instance, the EC has been widely used to characterize topological defects in LCs. − These defects have profound effects on the physical properties of the LC; , for instance, the total topological charge is correlated to the EC of the surface via the Gauss-Poincaré theorem (sum of all charges on a closed surface is equal to the EC of the surface) . In this work, we use topology to characterize the optical responses of nematic LC systems, which contain defects, as well as many other structural features.…”

Section: Introductionmentioning

confidence: 99%

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

et al. 2023

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Chemoresponsive liquid crystals (LCs) can be engineered to generate information-rich optical responses (in the form of space–time color and brightness patterns) when they are exposed to target gas contaminants. We investigate the use of topological descriptors (Euler characteristic, lacunarity, and fractal dimension) for extracting different types of information from these complex optical responses and show that these tools can enable the design of sensors and help gain insights into the physical phenomena governing sensor responses. We provide a holistic perspective of topological descriptors using the theory of Minkowski functionals and fractal analysis, which allows us to understand specific information that each descriptor extracts. We also show how to use the topological descriptors in conjunction with space–time filtration operations and color representations to enrich the information extracted. We demonstrate how these capabilities can be used in flexible ways to facilitate unsupervised machine learning (ML) tasks (clustering and visualization) and supervised tasks (regression and classification). We demonstrate the developments using real, high-throughput experimental data sets for functionalized LC films that are exposed to different gaseous environments. We show that the topological descriptors encode significant information and can be used to detect outliers in high-throughput data and visualize the temporal evolution of structure. Moreover, we show that the topological descriptors can be used to predict contaminant concentrations using simple ML models such as support vector machines; notably, these ML models can achieve accuracies comparable to those of powerful convolutional neural networks but with a much lower computational cost (from hours to seconds) and using less sophisticated computing hardware (CPUs instead of GPUs). This scalability enables the analysis of space–time data at high resolutions.

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Section: Introductionmentioning

confidence: 99%

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

et al. 2023

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“…Topological descriptors are capable of extracting information from multidimensional data and are tolerant to noise 14 ; in addition, topological descriptors can provide natural connections with physical phenomena that drives behavior. For instance, the EC has been widely used to characterize topological defects in LCs [15][16][17][18][19][20][21][22] . These defects have profound effects on the physical properties of the LC 23,24 ; for instance, the total topological charge is correlated to the EC of the surface via the Gauss-Poincaré theorem (sum of all charges on a closed surface is equal to the EC of the surface) 25 .…”

Section: Introductionmentioning

confidence: 99%

Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Jiang

Bao

Smith

et al. 2023

Preprint

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Chemoresponsive liquid crystals (LCs) can be engineered to generate information-rich optical responses (in the form of space-time color and brightness patterns) when exposed to target gas contaminants. We investigate the use of topological descriptors (Euler characteristic, lacunarity, and fractal dimension) for extracting different types of information from these complex optical responses and show that these tools can enable the design of sensors and help gain insights into physical phenomena governing sensor responses. We provide a holistic perspective of topological descriptors using theory of Minkowski functionals and fractal analysis, which allows us to understand specific information that each descriptor extracts. We also show how to use the topological descriptors in conjunction with space-time filtration operations and color representations to enrich the information extracted. We demonstrate how these capabilities can be used in flexible ways to facilitate unsupervised machine learning (ML) tasks (clustering and visualization) and supervised tasks (regression and classification). We demonstrate the developments using real, high-throughput experimental datasets for functionalized LC films that are exposed to different gaseous environments. We show that the topological descriptors encode significant information and can be used to detect outliers in high-throughput data and visualize the temporal evolution of topology. Moreover, we show that the topological descriptors can be used to predict contaminant concentrations using simple ML models such as support vector machines; notably, these ML models can achieve comparable accuracies to those of powerful convolutional neural networks but with a much lower computational cost (from hours to seconds) and using less sophisticated computing hardware (CPUs instead of GPUs). This scalability enables the analysis of space-time data at high resolutions.

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“…[26][27][28][29] Additionally, the rotational symmetry of LC textures can be manipulated through special electrode design. [30][31][32] Due to the thermal motion and fluidity of LCs, the obtained textures often deviate from or even violate the original design. In addition, the rotational symmetry is commonly restricted to triangular, square, and hexagonal lattices due to the relatively low free energy, hindering a direct and systematic comparison among LC textures of different rotational symmetries.…”

Section: Introductionmentioning

confidence: 99%

Tailoring on Rotational Symmetry of Liquid Crystal Domain Lattices

Wu,

2023

Advanced Physics Research

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Rotational symmetry is ubiquitous in nature. However, self‐assemblies of soft condensed matter such as liquid crystals (LCs) are entropy‐driven, making the tailoring of rotational symmetry challenging. Here, an approach is proposed to control the rotational symmetry of LC domain lattices based on the anchoring condition predesign and the orientational‐order inheritance during the nematic‐smectic phase transition. By this means, periodic and quasiperiodic LC textures with Ci symmetry (i = 2–6) determined by the preset alignment lattices are realized, and verified by symmetries of diffraction patterns. Topological analysis is carried out to disclose distinct evolutions of orders between disclination line textures and defect wall textures of different rotational symmetries. The influences of anchoring conditions, phase transitions, and mechanical stress on the self‐assembly of LCs, as well as the underlying mechanisms and dynamics, are investigated. This work realizes controllable rotational symmetry for large‐area self‐organized LCs and brings new insights to soft condensed matter.

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Crystal-like topological defect arrays in nematic liquid crystal

Cited by 6 publications

References 41 publications

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Scalable Extraction of Information from Spatiotemporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Scalable Extraction of Information from Spatio-Temporal Patterns of Chemoresponsive Liquid Crystals Using Topological Descriptors

Tailoring on Rotational Symmetry of Liquid Crystal Domain Lattices

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