Machine learning-aided analysis for complex local structure of liquid crystal polymers
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 microsc...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6841663/ https://www.ncbi.nlm.nih.gov/pubmed/31705002 http://dx.doi.org/10.1038/s41598-019-51238-1 |
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author | Doi, Hideo Takahashi, Kazuaki Z. Tagashira, Kenji Fukuda, Jun-ichi Aoyagi, Takeshi |
author_facet | Doi, Hideo Takahashi, Kazuaki Z. Tagashira, Kenji Fukuda, Jun-ichi Aoyagi, Takeshi |
author_sort | Doi, Hideo |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6841663 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68416632019-11-14 Machine learning-aided analysis for complex local structure of liquid crystal polymers Doi, Hideo Takahashi, Kazuaki Z. Tagashira, Kenji Fukuda, Jun-ichi Aoyagi, Takeshi Sci Rep Article 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. Nature Publishing Group UK 2019-11-08 /pmc/articles/PMC6841663/ /pubmed/31705002 http://dx.doi.org/10.1038/s41598-019-51238-1 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Doi, Hideo Takahashi, Kazuaki Z. Tagashira, Kenji Fukuda, Jun-ichi Aoyagi, Takeshi Machine learning-aided analysis for complex local structure of liquid crystal polymers |
title | Machine learning-aided analysis for complex local structure of liquid crystal polymers |
title_full | Machine learning-aided analysis for complex local structure of liquid crystal polymers |
title_fullStr | Machine learning-aided analysis for complex local structure of liquid crystal polymers |
title_full_unstemmed | Machine learning-aided analysis for complex local structure of liquid crystal polymers |
title_short | Machine learning-aided analysis for complex local structure of liquid crystal polymers |
title_sort | machine learning-aided analysis for complex local structure of liquid crystal polymers |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6841663/ https://www.ncbi.nlm.nih.gov/pubmed/31705002 http://dx.doi.org/10.1038/s41598-019-51238-1 |
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