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Machine learning-aided analysis for complex local structure of liquid crystal polymers.

ABSTRACT: Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure Analyzer (ML-LSA) is developed to classify the complex local mesoscopic structures of molecules that have not only simple atomistic group units but also rigid anisotropic functional groups such as mesogens. The proposed ML-LSA is applied to classifying the local structures of liquid crystal polymer (LCP) systems, which are of considerable scientific and technological interest because of their potential for sensors and soft actuators. A machine learning (ML) model is constructed from small, and thus computationally less costly, monodomain LCP trajectories. The ML model can distinguish nematic- and smectic-like monodomain structures with high accuracy. The ML-LSA is applied to large, complex quenched LCP structures, and the complex local structures are successfully classified as either nematic- or smectic-like. Furthermore, the results of the ML-LSA suggest the best order parameter for distinguishing the two mesogenic structures. Our ML model enables automatic and systematic analysis of the mesogenic structures without prior knowledge, and thus can overcome the difficulty of manually determining the specific order parameter required for the classification of complex structures.

SUBMITTER: Doi H

PROVIDER: S-EPMC6841663 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Machine learning-aided analysis for complex local structure of liquid crystal polymers.

Doi Hideo H Takahashi Kazuaki Z KZ Tagashira Kenji K Fukuda Jun-Ichi JI Aoyagi Takeshi T

Scientific reports 20191108 1

Elucidation of mesoscopic structures of molecular systems is of considerable scientific and technological interest for the development and optimization of advanced materials. Molecular dynamics simulations are a promising means of revealing macroscopic physical properties of materials from a microscopic viewpoint, but analysis of the resulting complex mesoscopic structures from microscopic information is a non-trivial and challenging task. In this study, a Machine Learning-aided Local Structure ...[more]

PMID: 31705002

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Project description:We reported recently that amphiphilic polymers can be assembled at interfaces created between aqueous phases and thermotropic liquid crystals (LCs) in ways that: (i) couple the organization of the polymer to the order of the LC and (ii) respond to changes in the properties of aqueous phases that can be characterized as changes in the optical appearance of the LC. This investigation sought to characterize the behavior of aqueous-LC interfaces decorated with uniaxially compressed thin films of polymers transferred by Langmuir-Schaefer (LS) transfer. Here, we report physicochemical characterization of interfaces created between aqueous phases and the thermotropic LC 4-cyano-4'-pentylbiphenyl (5CB) decorated with Langmuir films of a novel amphiphilic polymer (polymer 1), synthesized by the addition of hydrophobic and hydrophilic side chains to poly(2-vinyl-4,4'-dimethylazlactone). Initial characterization of this system resulted in the unexpected observation of uniform azimuthal alignment of 5CB after LS transfer of the polymer films to aqueous-5CB interfaces. This paper describes characterization of Langmuir films of polymer 1 hosted at aqueous-5CB interfaces as well as the results of our investigations into the origins of the uniform ordering of the LC observed upon LS transfer. Our results, when combined, support the conclusion that uniform azimuthal alignment of 5CB is the result of long-range ordering of polymer chains in the Langmuir films (in a preferred direction orthogonal to the direction of compression) that is generated during uniaxial compression of the films prior to LS transfer. Although past studies of Langmuir films of polymers at aqueous-air interfaces have demonstrated that in-plane alignment of polymer backbones can be induced by uniaxial compression, these past reports have generally made use of polymers with rigid backbones. One important outcome of this current study is thus the observation of anisotropy and long-range order in Langmuir films of a novel flexible polymer. A second important outcome is the observation that the existence, extent, and dynamics of this order can be identified and characterized optically by transfer of the Langmuir film to a thin film of LC. Additional characterization of Langmuir films of two other flexible polymers [poly(methyl methacrylate) and poly(vinyl stearate)] using this method also resulted in uniform azimuthal alignment of 5CB, suggesting that the generation of long-range order in uniaxially compressed Langmuir films of polymers may also occur more generally over a broader range of polymers with flexible backbones.

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Machine learning-aided analysis for complex local structure of liquid crystal polymers.

Publications

Machine learning-aided analysis for complex local structure of liquid crystal polymers.

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