High-throughput blue phase liquid crystal recognition based on convolutional neural network

Ya-qian ZHANG; Yong-feng CUI; Hao WANG; Wan-li HE; Lei ZHANG; Zhou YANG; Hui CAO; Dong WANG; Yu-zhan LI

doi:10.37188/CJLCD.2021-0315

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High-throughput blue phase liquid crystal recognition based on convolutional neural network

LCD/OLED Display Technology | 更新时间：2022-08-10

- High-throughput blue phase liquid crystal recognition based on convolutional neural network
- Chinese Journal of Liquid Crystals and Displays Vol. 37, Issue 8, Pages: 972-979(2022)
- 作者机构：
  
  北京科技大学材料科学与工程学院，北京 100083
- 作者简介：
- 基金信息：
  
  National Key R&D Program of China(2018YFB0703703);National Natural Science Foundation of China(61370048;51673023;51773017;51973017)
- DOI：10.37188/CJLCD.2021-0315
  CLC： O753⁺.2
- Received：01 December 2021，
  
  Revised：27 December 2021，
  
  Published：05 August 2022
- 稿件说明：
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Ya-qian ZHANG, Yong-feng CUI, Hao WANG, et al. High-throughput blue phase liquid crystal recognition based on convolutional neural network[J]. Chinese journal of liquid crystals and displays, 2022, 37(8): 972-979.
DOI：

Ya-qian ZHANG, Yong-feng CUI, Hao WANG, et al. High-throughput blue phase liquid crystal recognition based on convolutional neural network[J]. Chinese journal of liquid crystals and displays, 2022, 37(8): 972-979. DOI： 10.37188/CJLCD.2021-0315.

摘要

随着液晶显示技术的发展，蓝相液晶由于存在诸多优点而进入研究人员的视野，并吸引着人们的持续关注，但是其在研究和实用过程中存在许多困难，例如蓝相只在很窄的温度区间内存在，所以对于蓝相液晶的快速识别和蓝相存在温度区间的快速计算就显得格外重要。本文通过机器学习算法训练得到模型，结合Labview软件可以实现液晶相态的快速识别和蓝相温域的快速读取计算，在实验过程中对159 840张样品点相态图像的整体识别准确率在93%以上。本文研究结果可以更高效识别蓝相和计算蓝相温域，更快速筛选合适的蓝相液晶配方，从而提高实验研究效率，推动蓝相液晶早日应用于显示器件。

Abstract

With the development of liquid crystal display technology， blue phase liquid crystal has entered the researcher’s field of vision due to its advantages and has attracted continuous attention. However， there are many difficulties in its research and practical application. For example， the blue phase only exists in a very narrow temperature range， so the rapid identification of the blue phase liquid crystal and the rapid calculation of blue phase temperature range are extremely important. In this paper， a model trained by a machine learning algorithm combined with Labview software can realize the rapid recognition of the liquid crystal phase state and the rapid reading and calculation of the blue phase temperature range. During the experiment， the overall recognition accuracy of 159 840 sample point phase images was above 93%. The research results of this paper can help to effectively identify the blue phase， quickly obtain the blue phase temperature range， and quickly screen the applicable blue phase liquid crystal formula， so as to improve the research efficiency of blue phase materials and to promote their application in display devices.

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references

CHEN Y ， WU S T . Recent advances on polymer-stabilized blue phase liquid crystal materials and devices ［J］. Journal of Applied Polymer Science ， 2014 ， 131 （ 13 ）： 40556 . doi: 10.1002/app.40556 http://dx.doi.org/10.1002/app.40556

CAO W Y ， MUÑOZ A ， PALFFY-MUHORAY P ， et al . Lasing in a three-dimensional photonic crystal of the liquid crystal blue phase II ［J］. Nature Materials ， 2002 ， 1 （ 2 ）： 111 - 113 . doi: 10.1038/nmat727 http://dx.doi.org/10.1038/nmat727

COLES H ， MORRIS S . Liquid-crystal lasers ［J］. Nature Photonics ， 2010 ， 4 （ 10 ）： 676 - 685 . doi: 10.1038/nphoton.2010.184 http://dx.doi.org/10.1038/nphoton.2010.184

WANG L ， LI Q . Stimuli-directing self-organized 3D liquid-crystalline nanostructures： from materials design to photonic applications ［J］. Advanced Functional Materials ， 2016 ， 26 （ 1 ）： 10 - 28 . doi: 10.1002/adfm.201502071 http://dx.doi.org/10.1002/adfm.201502071

WANG L ， URBAS A M ， LI Q . Nature-inspired emerging chiral liquid crystal nanostructures： from molecular self-assembly to DNA mesophase and nanocolloids ［J］. Advanced Materials ， 2020 ， 32 （ 41 ）： 1801335 . doi: 10.1002/adma.201801335 http://dx.doi.org/10.1002/adma.201801335

崔永杰，钟佳鑫，廖勋凡，等 . 液晶分子在有机太阳能电池中的应用研究进展［J］. 应用化学， 2021 ， 38 （ 10 ）： 1326 - 1339 . doi: 10.19894/j.issn.1000-0518.210359 http://dx.doi.org/10.19894/j.issn.1000-0518.210359

CUI Y J ， ZHONG J X ， LIAO X F ， et al . Research progress of liquid crystal molecules for application in organic solar cells ［J］. Chinese Journal of Applied Chemistry ， 2021 ， 38 （ 10 ）： 1326 - 1339 . （in Chinese） . doi: 10.19894/j.issn.1000-0518.210359 http://dx.doi.org/10.19894/j.issn.1000-0518.210359

YOKOYAMA S ， MASHIKO S ， KIKUCHI H ， et al . Laser emission from a polymer-stabilized liquid-crystalline blue phase ［J］. Advanced Materials ， 2006 ， 18 （ 1 ）： 48 - 51 . doi: 10.1002/adma.200501355 http://dx.doi.org/10.1002/adma.200501355

STRATFORD K ， HENRICH O ， LINTUVUORI J S ， et al . Self-assembly of colloid-cholesteric composites provides a possible route to switchable optical materials ［J］. Nature Communications ， 2014 ， 5 ： 3954 . doi: 10.1038/ncomms4954 http://dx.doi.org/10.1038/ncomms4954

BUKUSOGLU E ， WANG X G ， MARTINEZ-GONZALEZ J A ， et al . Stimuli-responsive cubosomes formed from blue phase liquid crystals ［J］. Advanced Materials ， 2015 ， 27 （ 43 ）： 6892 - 6898 . doi: 10.1002/adma.201503484 http://dx.doi.org/10.1002/adma.201503484

MARTÍNEZ-GONZÁLEZ J A ， ZHOU Y ， RAHIMI M ， et al . Blue-phase liquid crystal droplets ［J］. Proceedings of the National Academy Science of the United States of America ， 2015 ， 112 （ 43 ）： 13195 - 13200 . doi: 10.1073/pnas.1514251112 http://dx.doi.org/10.1073/pnas.1514251112

KIKUCHI H ， YOKOTA M ， HISAKADO Y ， et al . Polymer-stabilized liquid crystal blue phases ［J］. Nature Materials ， 2002 ， 1 （ 1 ）： 64 - 68 . doi: 10.1038/nmat712 http://dx.doi.org/10.1038/nmat712

CASTLES F ， DAY F V ， MORRIS S M ， et al . Blue-phase templated fabrication of three-dimensional nanostructures for photonic applications ［J］. Nature Materials ， 2012 ， 11 （ 7 ）： 599 - 603 . doi: 10.1038/nmat3330 http://dx.doi.org/10.1038/nmat3330

陈斌，东一舟，朱晋宁 . 非控场景下主成分稀疏表示与低秩分解的人脸识别［J］. 液晶与显示， 2019 ， 34 （ 8 ）： 816 - 824 . doi: 10.3788/yjyxs20193408.0816 http://dx.doi.org/10.3788/yjyxs20193408.0816

CHEN B ， DONG Y Z ， ZHU J N . Face recognition based on sparse representation of principal components and low rank decomposition in uncontrolled scenes ［J］. Chinese Journal of Liquid Crystals and Displays ， 2019 ， 34 （ 8 ）： 816 - 824 . （in Chinese） . doi: 10.3788/yjyxs20193408.0816 http://dx.doi.org/10.3788/yjyxs20193408.0816

LECUN Y ， BOTTOU L ， BENGIO Y ， et al . Gradient-based learning applied to document recognition ［J］. Proceedings of the IEEE ， 1998 ， 86 （ 11 ）： 2278 - 2324 . doi: 10.1109/5.726791 http://dx.doi.org/10.1109/5.726791

ALOM M Z ， TAHA T M ， YAKOPCIC C ， et al . The history began from alexnet： a comprehensive survey on deep learning approaches ［J］. arXiv ， 2018 ： 1803 .01164. doi: 10.1109/ijcnn.2018.8489635 http://dx.doi.org/10.1109/ijcnn.2018.8489635

ZEILER M D ， FERGUS R . Visualizing and understanding convolutional networks ［C］// Proceedings of the 2014 13th European Conference on Computer Vision . Zurich ： Springer ， 2014 ： 818 - 833 . doi: 10.1007/978-3-319-10590-1_53 http://dx.doi.org/10.1007/978-3-319-10590-1_53

SZEGEDY C ， LIU W ， JIA Y Q ， et al . Going deeper with convolutions ［C］// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Boston ： IEEE ， 2015 ： 1 - 9 . doi: 10.1109/cvpr.2015.7298594 http://dx.doi.org/10.1109/cvpr.2015.7298594

SIMONYAN K ， ZISSERMAN A . Very deep convolutional networks for large-scale image recognition ［J］. arXiv ， 2014 ： 1409 .1556.

SZEGEDY C ， IOFFE S ， VANHOUCKE V ， et al . Inception-v4， inception-ResNet and the impact of residual connections on learning ［C］// Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence . San Francisco ： ACM ， 2017 ： 4278 - 4284 . doi: 10.1609/aaai.v31i1.11231 http://dx.doi.org/10.1609/aaai.v31i1.11231

XIA X L ， XU C ， NAN B . Inception-v3 for flower classification ［C］// Proceedings of 2017 2nd IEEE International Conference on Image， Vision and Computing （ICIVC） . Chengdu ： IEEE ， 2017 ： 783 - 787 . doi: 10.1109/icivc.2017.7984661 http://dx.doi.org/10.1109/icivc.2017.7984661

XIE S N ， GIRSHICK R ， DOLLÁR P ， et al . Aggregated residual transformations for deep neural networks ［C］// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Honolulu ： IEEE ， 2017 ： 5987 - 5995 . doi: 10.1109/cvpr.2017.634 http://dx.doi.org/10.1109/cvpr.2017.634

HUANG G ， LIU Z ， MAATEN LVAN DER ， et al . Densely connected convolutional networks ［C］// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Honolulu ： IEEE ， 2017 ： 2261 - 2269 . doi: 10.1109/cvpr.2017.243 http://dx.doi.org/10.1109/cvpr.2017.243

CHOLLET F . Xception： deep learning with depthwise separable convolutions ［C］// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR） . Honolulu ： IEEE ， 2017 ： 1800 - 1807 . doi: 10.1109/cvpr.2017.195 http://dx.doi.org/10.1109/cvpr.2017.195

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