Testing different supervised machine learning architectures for the classification of liquid crystals

References

• Collings PJ, Goodby JW. Introduction to liquid crystals: chemistry and physics. 2nd ed. Boca Raton: CRC Press; 2020. DOI:10.1201/9781315098340
   Google Scholar
• Singh S, Dunmur PDA. Liquid crystals: fundamentals. Singapore: World Scientific Publishing; 2002. DOI:10.1142/4369
   Google Scholar
• Dierking I. Textures of liquid crystals. Weinheim: Wiley-VCH; 2003. DOI:10.1002/3527602054
   Google Scholar
• Kumar S, ed. Liquid crystals: experimental study of physical properties and phase transitions. Cambridge: Cambridge University Press; 2000.
   Google Scholar
• Sigaki HYD, Lenzi EK, Zola RS, et al. Learning physical properties of liquid crystals with deep convolutional neural networks. Sci Rep. 2020;10:7664. DOI: 10.1038/s41598-020-63662-9
   PubMed Web of Science ®Google Scholar
• Sigaki HYD, de Souza RF, de Souza RT, et al. Estimating physical properties from liquid crystal textures via machine learning and complexity-entropy methods. Phys Rev E. 2019;99(1):013311. DOI:10.1103/PhysRevE.99.013311
   PubMed Web of Science ®Google Scholar
• Butler KT, Davies DW, Cartwright H, et al. Machine learning for molecular and materials science. Nature. 2018;559(7715):547. DOI:10.1038/s41586-018-0337-2
   PubMed Web of Science ®Google Scholar
• Schmidt J, Marques MRG, Botti S, et al. Recent advances and applications of machine learning in solid state materials science. NPJ Comp Mat. 2019;5:83. DOI: 10.1038/s41524-019-0221-0
   Web of Science ®Google Scholar
• Jackson NE, Webb MA, de Pablo JJ. Recent advances in machine learning towards multiscale soft materials design. Curr Opin Chem Eng. 2019;23:106. DOI: 10.1016/j.coche.2019.03.005
   Web of Science ®Google Scholar
• Pessa AAB, Zola RS, Perc M, et al. Determining liquid crystal properties with ordinal networks and machine learning. Chaos, Solit Fractals. 2022;154:111607. DOI:10.1016/j.chaos.2021.111607
   Web of Science ®Google Scholar
• Chen C-H, Tanaka K, Funatsu K. Random forest model with combined features: a practical approach to predict liquid-crystalline property. Mol Inf. 2019;38(4):1800095. DOI:10.1002/minf.201800095
   Web of Science ®Google Scholar
• Inokuchi T, Okamoto R, Arai N. Predicting molecular ordering in a binary liquid crystal using machine learning. Liq Cryst. 2020;47(3):438. DOI:10.1080/02678292.2019.1656293
   Web of Science ®Google Scholar
• Le TC, Tran N. Using machine learning to predict the self-assembled nanostructures of monoolein and phytantriol as a function of temperature and fatty acid additives for effective lipid-based delivery systems. ACS Appl Nano Mater. 2019;2(3):1637. DOI:10.1021/acsanm.9b00075
   Web of Science ®Google Scholar
• Walters M, Wei Q, Chen JZY. Machine learning topological defects of confined liquid crystals in two dimensions. Phys Rev E. 2019;99(6):062701. DOI:10.1103/PhysRevE.99.062701
   PubMed Web of Science ®Google Scholar
• Minor EN, Howard SD, Green AAS, et al. End-to-end machine learning for experimental physics: using simulated data to train a neural network for object detection in video microscopy. Soft Matter. 2020;16:1751. DOI: 10.1039/C9SM01979K
   PubMed Web of Science ®Google Scholar
• Colen J, Han M, Zhang R, et al. Machine learning active-nematic hydrodynamics. PNAS. 2021;118:e2016708118. DOI: 10.1073/pnas.2016708118
   PubMed Web of Science ®Google Scholar
• Hedlund E, Hedlund K, Green A, et al. Detection of islands and droplets on smectic films using machine learning. Phys Fluids. 2022;34:103608. DOI: 10.1063/5.0117358
   Web of Science ®Google Scholar
• He W-L, Cui Y-F, Luo S-G, et al. High-throughput preparation and machine learning screening of a blue-phase liquid crystal based on inkjet printing. Molecules. 2022;27(20):6938. DOI:10.3390/molecules27206938
   PubMed Web of Science ®Google Scholar
• Zhang Y-G, Cui Y-F, Hao W, et al. High-throughput blue phase liquid crystal recognition based on convolutional neural network. Chin J Liq Cryst Disp. 2022;36:972. DOI: 10.37188/CJLCD.2021-0315
   Web of Science ®Google Scholar
• Nayani K, Yang Y, Yu H, et al. Areas of opportunity related to design of chemical and biological sensors based on liquid crystals. Liq Cryst Today. 2020;29(2):24. DOI:10.1080/1358314X.2020.1819624
   Web of Science ®Google Scholar
• Cao Y, Yu H, Abbott NL, et al. Machine learning algorithms for liquid crystal-based sensors. ACS Sens. 2018;3(11):2237. DOI:10.1021/acssensors.8b00100
   PubMed Web of Science ®Google Scholar
• Bao N, Jiang S, Smith A, et al. Sensing gas mixtures by analyzing the spatiotemporal optical responses of liquid crystals using 3D convolutional neural networks. ACS Sens. 2022;7:2545. DOI: 10.1021/acssensors.2c00362
   PubMed Web of Science ®Google Scholar
• Ramou E, Palma SICJ, Roque ACA. Nanoscale events on cyanobiphenyl-based self-assembled droplets triggered by gas analytes. ACS Appl Mater Interfaces. 2022;14:6261. DOI: 10.1021/acsami.1c24721
   PubMed Web of Science ®Google Scholar
• Xu Y, Rather AM, Song S, et al. Ultrasensitive and selective detection of SARS-CoV-2 using thermotropic liquid crystals and image-based machine learning. Cell Rep Phys Sci. 2020;1(12):100276. DOI:10.1016/j.xcrp.2020.100276
   PubMedGoogle Scholar
• Jiang S, Noh JH, Park C, et al. Using machine learning and liquid crystal droplets to identify and quantify endotoxins from different bacterial species. Analyst. 2021;146(4):1224. DOI:10.1039/D0AN02220A
   PubMed Web of Science ®Google Scholar
• Matysiewicz M, Neumann T, Nowak RM, et al. Automatic recognition of thermographic examinations for early detection of breast cancer. Proc. SPIE 10031, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments; 2016. p.100312X. DOI:10.1117/12.2249067
   Google Scholar
• Kang SB, Lee JH, Song KY, et al. Automatic defect classification of TFT-LCD panels using machine learning. IEEE International Symposium on Industrial Electronics (ISlE 2009) Seoul Olympic Parktel, Seoul, Korea July 5-8; 2009. p. 2175.
   Google Scholar
• Zuvela P, Lovric M, Yousefian-Jazi A, et al. Ensemble learning approaches to data imbalance and competing objectives in design of an industrial machine vision system. Ind Eng Chem Res. 2020;59(10):4636. DOI:10.1021/acs.iecr.9b05766
   Web of Science ®Google Scholar
• Liu Y, Lu H-P, Lai C-H. A novel attention-based multi-modal modeling technique on mixed type data for improving TFT-LCD repair process. IEEE Access. 2022;10:33026. DOI: 10.1109/ACCESS.2022.3158952
   Web of Science ®Google Scholar
• Dierking I, Dominguez J, Harbon J, et al. Classification of liquid crystal textures using convolutional neural networks. Liq Cryst. 2022;1–15. DOI:10.1080/02678292.2022.2150790
   Web of Science ®Google Scholar
• Dierking I, Dominguez J, Harbon J, et al. Deep learning techniques for the localization and classification of liquid crystal phase transitions. Front Soft Matter. 2023;3:1114551. DOI: 10.3389/frsfm.2023.1114551
   Google Scholar
• Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge (MA): MIT Press; 2016.
   Google Scholar
• Murphy KP. Machine learning: a probabilistic perspective. Cambridge (MA): The MIT Press; 2015.
   Google Scholar
• Mitchell TM. Machine learning. New York (NY): WCB/McGraw‐Hill; 1997.
   Google Scholar
• Kingma DP, Adam JB. A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014 - arxiv.org; 2017.
   Google Scholar
• Lever J, Krzywinski M, Altman N. Model selection and overfitting. Nat Methods. 2016;13:703. DOI: 10.1038/nmeth.3968
   Web of Science ®Google Scholar
• Lecun Y, Bottou L, Bengio Y, et al. Gradient‐based learning applied to document recognition. Proc IEEE. 1998;86:2278. DOI: 10.1109/5.726791
   Web of Science ®Google Scholar
• Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. CoRR; abs/1409.4842; 2014. Available from: http://arxiv.org/abs/1409.4842
   Google Scholar
• Dierking I, Giesselmann F, Kusserow J, et al. Properties of higher-ordered ferroelectric liquid crystal phases of a homologues series. Liq Cryst. 1994;17:243. DOI: 10.1080/02678299408036564
   Web of Science ®Google Scholar
• Schacht J, Dierking I, Giesselmann F, et al. Mesomorphic properties of a homologues series of chiral liquid crystals containing the α-chloroester group. Liq Cryst. 1995;19:151. DOI: 10.1080/02678299508031964
   Web of Science ®Google Scholar
• VideoLAN VLC media player. n.d. Available from: https://www.videolan.org/vlc/
   Google Scholar
• Keras API. n.d. Available from: https://keras.io/
   Google Scholar
• Google Colaboratory. n.d. Available from: https://colab.research.google.com/
   Google Scholar
• Meyer RB, Liebert L, Strzelecki L, et al. Ferroelectric liquid crystals. J de Phys Lett. 1975;36(3):69. DOI:10.1051/jphyslet:0197500360306900
   Google Scholar
• Betts R, Dierking I. In preparation.
   Google Scholar
• Imrie CT, Walker R, Storey JMD, et al. Liquid crystal dimers and smectic phases from the intercalated to the twist-bend. Crystals. 2022;12(9):1245. DOI:10.3390/cryst12091245
   Web of Science ®Google Scholar
• Chen X, Korblova E, Dong D, et al. First-principles experimental demonstration of ferroelectricity in a thermotropic nematic liquid crystal: spontaneous polar domains and striking electro-optics. PNAS. 2020;117:14021. DOI: 10.1073/pnas.2002290117
   PubMed Web of Science ®Google Scholar