Machine learning studies for liquid crystal texture recognition

References

• Mjolsness E, DeCoste D. Machine learning for science: state of the art and future prospects. Science. 2001;293(5537):2051–2055. doi: 10.1126/science.293.5537.2051
   PubMed Web of Science ®Google Scholar
• Baldi P, Sadowski P, Whiteson D. Searching for exotic particles in high-energy physics with deep learning. Nat Commun. 2014;5(1):4308. doi: 10.1038/ncomms5308
   PubMedGoogle Scholar
• Chen X, Xu S, Shabani S, et al. Machine learning for optical scanning probe nanoscopy. Adv Mat. 2022;2109171(34):1–15. doi: 10.1002/adma.202109171
   Google Scholar
• Deng DL, Li X, Das S, et al. Machine learning topological states. Phys Rev B. 2017;96(19):195145. doi: 10.1103/PhysRevB.96.195145
   Web of Science ®Google Scholar
• Carrasquilla J, Melko RG. Machine learning phases of matter. Nat Phys. 2017;13(5):431–434. doi: 10.1038/nphys4035
   Web of Science ®Google Scholar
• Bach MJS, Golubeva A, Melko RG. Machine learning vortices at the Kosterlitz-Thouless transition. Phys Rev B. 2018;97(4):045207. doi: 10.1103/PhysRevB.97.045207
   Web of Science ®Google Scholar
• Wang C, Zhai H. Machine learning of frustrated classical spin models. Phys Rev B. 2017;96(14):144432. doi: 10.1103/PhysRevB.96.144432
   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
• Sigaki H, de Souza R, de Souza R, 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
• Osiecka-Drewniak N, Czarnecki MA, Galewski Z. Investigation of phase transitions in liquid crystal 12BBAA using window clustering of infrared spectra. J Mol Liq. 2021;341:117233. doi: 10.1016/j.molliq.2021.117233
   Web of Science ®Google Scholar
• Tarca AL, Carey VJ, Chen XW, et al. Machine learning and its application to biology. PLoS Comput Biol. 2007;3(6):0953–0963. doi: 10.1371/journal.pcbi.0030116
   Web of Science ®Google Scholar
• Aghamiri SS, Amin R, Helikar T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J Pharmacokin Pharmacodyn. 2022;49(1):19–37. doi: 10.1007/s10928-021-09790-9
   PubMed Web of Science ®Google Scholar
• Pessa AAB, Zola RS, Perc M, et al. Determining liquid crystal properties with ordinal networks and machine learning. Chaos, Solitons & Fractals. 2022;154:111607. doi: 10.1016/j.chaos.2021.111607
   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 Matt. 2020;16(7):1751. doi: 10.1039/C9SM01979K
   PubMed Web of Science ®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(1):7664. doi: 10.1038/s41598-020-63662-9
   PubMed Web of Science ®Google Scholar
• Osiecka-Drewniak N, Galewski Z, Juszyńska-Gałązka E. Distinguish focal-conic fan texture of smectic a from focal conic fan texture of smectic B. Cryst. 2023;13(8):1187. doi: 10.3390/cryst13081187
   Web of Science ®Google Scholar
• Orlikowska H, Sobolewska A, Miniewicz A, et al. Application of the novel dynamic thermo-optical analysis for identification of the sequence of mesophases in thermotropic liquid crystal. Liq Cryst. 2017;44(7):1157–1164. doi: 10.1080/02678292.2016.1269371
   Web of Science ®Google Scholar
• Piwowarczyk M, Korbecka I, Sokolnicki J, et al. Esters of 4-nitrocinnamic acids and 4-halogene-4’-hydroxyazobenzenes – synthesis, mesogenic and optical studies. Phase Trans. 2019;92(3):217–228. doi: 10.1080/01411594.2018.1563790
   Web of Science ®Google Scholar
• Pietrzyk S, Yevchenko T, Dardas D, et al. Phase transitions and physical properties by a colour texture analysis: results for liquid crystals. J Mol Liq. 2022;362:119699. doi: 10.1016/j.molliq.2022.119699
   Web of Science ®Google Scholar
• Betts R, Dierking I. Machine learning classification of polar sub-phases in liquid crystal MHPOBC. Soft Matt. 2023;19(39):7502–7512. doi: 10.1039/D3SM00902E
   PubMed 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;50(7–10):1526–1540. doi: 10.1080/02678292.2022.2150790
   Web of Science ®Google Scholar
• Dierking I, Dominguez J, Harbon J, et al. Testing different supervised machine learning architectures for the classification of liquid crystals. Liq Cryst. 2023;50(7–10):1461–1477. doi: 10.1080/02678292.2023.2221654
   Web of Science ®Google Scholar
• Drzewicz A, Juszyńska-Gałązka E, Zając W, et al. Non-isothermal and isothermal cold crystallization of glass-forming chiral smectic liquid crystal (S)-4′-(1-methyloctyloxycarbonyl) biphenyl-4-yl 4-[7-(2,2,3,3,4,4,4-heptafluorobutoxy) heptyl-1-oxy]-benzoate. J Mol Liq. 2020;319(319):114153. doi: 10.1016/j.molliq.2020.114153
   Google Scholar
• Drzewicz A, Jasiurkowska-Delaporte M, Juszyńska-Gałązka E, et al. On the relaxation dynamics of a double glass-forming antiferroelectric liquid crystal. Phys Chem Chem Phys. 2021;23(14):8673. doi: 10.1039/D0CP06203K
   PubMed Web of Science ®Google Scholar
• Jasiurkowska-Delaporte M, Juszyńska E, Ł K, et al. Signatures of glass transition in partially ordered phases. Liq Cryst. 2013;40(10):1436–1442. doi: 10.1080/02678292.2013.828330
   Web of Science ®Google Scholar
• D’Have K, Rudquist P, Lagerwall ST, et al. Solution of the dark state problem in antiferroelectric liquid crystal displays. Appl Phys Lett. 2000;76(24):3528–3530. doi: 10.1063/1.126696
   Web of Science ®Google Scholar
• Milewska K, Drzewiński W, Cerwiński M, et al. Design, synthesis and mesomorphic properties of chiral benzoates and fluorobenzoates with direct SmCA*-Iso phase transition. Liq Cryst. 2015;42:1601–1611. doi: 10.1080/02678292.2015.1078916
   Web of Science ®Google Scholar
• Bradski G. The OpenCV Library. J Soft Tools. 2000;120:122–125.
   Google Scholar
• Gulli A, Kapoor A, Pal S. Deep learning with TensorFlow 2 and keras –. 2nd ed. Birmingham (UK): Packt; 2019.
   Google Scholar
• Buda M, Maki A, Mazurkowski MA. A systematic study of the class imbalance problem in convolutional neural networks. Neural Net. 2018;106:249–259. doi: 10.1016/j.neunet.2018.07.011
   PubMed Web of Science ®Google Scholar
• Ou G, Murphey YL. Multi-class pattern classification using neural networks. Pattern Rec. 2007;40(1):4–18. doi: 10.1016/j.patcog.2006.04.041
   Web of Science ®Google Scholar
• Mazurkwski MA, Habas PA, Zurada JM, et al. Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance. Neural Net. 2008;21(2–3):427–436. doi: 10.1016/j.neunet.2007.12.031
   PubMed Web of Science ®Google Scholar
• Massalska-Arodź M, Janik JA. Fractal-like scaling in liquid crystals. Odd–even effect of scaling exponents in the 4,4′-di- n -alkyloxyazoxybenzene homologous series. Liq Cryst. 1991;10(1):135–140. doi: 10.1080/02678299108028237
   Web of Science ®Google Scholar
• Dierking I. Liquid crystals, fractals and art. Liq Cryst Tod. 2012;21(3):54–65. doi: 10.1080/1358314X.2012.720515
   Google Scholar
• Zhao D, Bu L, Alippi C, et al. A Kolmogorov-Smirnov test to detect changes in stationarity in big data. IFAC. 2017;50(1):14260–14265. doi: 10.1016/j.ifacol.2017.08.1821
   Google Scholar
• Dierking I. Textures of liquid crystals. Weinheim: Wiley-VCH; 2003.
   Google Scholar
• Goodby JW, Pindak R. Characterization of the hexatic B and crystal B phases by optical microscopy. Mol Cryst Liq Cryst. 1981;75(1):233–247. doi: 10.1080/00268948108073618
   Web of Science ®Google Scholar
• Wang F, Elbadawi M, Liu Tsilova S, et al. Machine learning to empower electrohydrodynamic processing. Mat Scie Eng C. 2022;132:112553. doi: 10.1016/j.msec.2021.112553
   PubMed Web of Science ®Google Scholar
• Zhou H, Wang R, Wang C. A novel extended local-binary-pattern operator for texture analysis. Inform Scie. 2008;178(22):4314–4325. doi: 10.1016/j.ins.2008.07.015
   Web of Science ®Google Scholar
• Qian X, Hua XS, Chen P, et al. PLBP: An effective local binary patterns texture descriptor with pyramid representation. Pat Rec. 2011;44(10–11):2502–2515. doi: 10.1016/j.patcog.2011.03.029
   Web of Science ®Google Scholar
• Dieking I, Dominguez J, Harbon J, et al. Deep learning techniques for the localization and classification of liquid crystal phase transitions. Front Soft Mat. 2022;3:1114551. doi: 10.3389/frsfm.2023.1114551
   Google Scholar
• Sigaki HYD, Lenzi EK, Zola RS, et al. Learning physical properties of liquid crystals with deep convolutional neural networks. Scie Rep. 2020;10(1):7664. doi: 10.1038/s41598-020-63662-9
   PubMed Web of Science ®Google Scholar
• Smith AD, Abbott N, Zavala VM. Convolutional network analysis of optical micrographs for liquid crystal sensors. J Phys Chem C. 2020;124(28):15152–15161. doi: 10.1021/acs.jpcc.0c01942
   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):2237–2245. doi: 10.1021/acssensors.8b00100
   PubMed Web of Science ®Google Scholar
• Gu J, Wang Z, Kuen J, et al. Recent advances in convolutional neural networks. Pattern Rec. 2018;77:354–377. doi: 10.1016/j.patcog.2017.10.013
   Web of Science ®Google Scholar
• Jay Kuo CC. Understanding convolutional neural networks with a mathematical model. J Vis Comm Image Rep. 2016;41:406–413. doi: 10.1016/j.jvcir.2016.11.003
   Web of Science ®Google Scholar