Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information

Abstract Topological defects in liquid crystals (LCs) dominate molecular alignment/motion in many cases. Here, the neural network (NN) function has been introduced to predict the LC orientation condition (orientation angle and order parameter) at local positions around topological defects from the p...

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Autores principales:	Haruka Sakanoue, Yuki Hayashi, Kenji Katayama
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6bde48d734944dd1b42d1c42bdaba4b1
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spelling	oai:doaj.org-article:6bde48d734944dd1b42d1c42bdaba4b12021-12-02T13:40:51ZInference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information10.1038/s41598-021-88535-72045-2322https://doaj.org/article/6bde48d734944dd1b42d1c42bdaba4b12021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-88535-7https://doaj.org/toc/2045-2322Abstract Topological defects in liquid crystals (LCs) dominate molecular alignment/motion in many cases. Here, the neural network (NN) function has been introduced to predict the LC orientation condition (orientation angle and order parameter) at local positions around topological defects from the phase/polarization microscopic color images. The NN function was trained in advance by using the color information of an LC in a planar alignment cell for different orientation angles and temperatures. The photo-induced changes of LC molecules around topological defects observed by the time-resolved measurement was converted into the image sequences of the orientation angle and the order parameter change. We found that each pair of brushes with different colors around topological defects showed different orientation angle and ordering changes. The photo-induced change was triggered by the photoisomerization reaction of molecules, and one pair of brushes increased in its order parameter just after light irradiation, causing gradual rotation in the brush. The molecules in the other pair of brushes were disordered and rotated by the effect of the initially affected region. This combination approach of the time-resolved phase/polarization microscopy and the NN function can provide detailed information on the molecular alignment dynamics around the topological defects.Haruka SakanoueYuki HayashiKenji KatayamaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Haruka Sakanoue Yuki Hayashi Kenji Katayama Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information
description	Abstract Topological defects in liquid crystals (LCs) dominate molecular alignment/motion in many cases. Here, the neural network (NN) function has been introduced to predict the LC orientation condition (orientation angle and order parameter) at local positions around topological defects from the phase/polarization microscopic color images. The NN function was trained in advance by using the color information of an LC in a planar alignment cell for different orientation angles and temperatures. The photo-induced changes of LC molecules around topological defects observed by the time-resolved measurement was converted into the image sequences of the orientation angle and the order parameter change. We found that each pair of brushes with different colors around topological defects showed different orientation angle and ordering changes. The photo-induced change was triggered by the photoisomerization reaction of molecules, and one pair of brushes increased in its order parameter just after light irradiation, causing gradual rotation in the brush. The molecules in the other pair of brushes were disordered and rotated by the effect of the initially affected region. This combination approach of the time-resolved phase/polarization microscopy and the NN function can provide detailed information on the molecular alignment dynamics around the topological defects.
format	article
author	Haruka Sakanoue Yuki Hayashi Kenji Katayama
author_facet	Haruka Sakanoue Yuki Hayashi Kenji Katayama
author_sort	Haruka Sakanoue
title	Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information
title_short	Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information
title_full	Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information
title_fullStr	Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information
title_full_unstemmed	Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information
title_sort	inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/6bde48d734944dd1b42d1c42bdaba4b1
work_keys_str_mv	AT harukasakanoue inferenceofmolecularorientationorderingchangenearbytopologicaldefectsbytheneuralnetworkfunctionfromthemicroscopiccolorinformation AT yukihayashi inferenceofmolecularorientationorderingchangenearbytopologicaldefectsbytheneuralnetworkfunctionfromthemicroscopiccolorinformation AT kenjikatayama inferenceofmolecularorientationorderingchangenearbytopologicaldefectsbytheneuralnetworkfunctionfromthemicroscopiccolorinformation
_version_	1718392620900679680

Inference of molecular orientation/ordering change nearby topological defects by the neural network function from the microscopic color information

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