Predicting molecular ordering in a binary liquid crystal using machine learning

References

• Norman D. Design, business models, and human-technology teamwork. Res-Technol Manage. 2017;60:26–30.
   Web of Science ®Google Scholar
• Jia W, Li Y, Qu R, et al. Automatic food detection in egocentric images using artificial intelligence technology. Public Health Nutr. 2018;1:12.
   Google Scholar
• Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science. 2015;349:255–260.
   PubMed Web of Science ®Google Scholar
• Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16:321.
   PubMed Web of Science ®Google Scholar
• Kourou K, Exarchos TP, Exarchos KP, et al. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.
   PubMed Web of Science ®Google Scholar
• Li YJ, Savan A, Kostka A, et al. Accelerated atomic-scale exploration of phase evolution in compositionally complex materials. Mater Horiz. 2018;5:86–92.
   Web of Science ®Google Scholar
• Sommer R, Paxson V. Outside the closed world: On using machine learning for network intrusion detection. IEEE Secur Priv. 2010;16-19;305-316..
   Google Scholar
• Krauss C, Do XA, Huck N. Deep neural networks, gradient-boosted trees, random forests: statistical arbitrage on the s and p 500. Eur J Oper Res. 2017;259:689–702.
   Web of Science ®Google Scholar
• Seko A, Togo A, Hayashi H, et al. Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and bayesian optimization. Phys Rev Lett. 2015;115:205901.
   PubMed Web of Science ®Google Scholar
• Takahashi K, Tanaka Y. Material synthesis and design from first principle calculations and machine learning. Comput Mater Sci. 2016;112:364–367.
   Web of Science ®Google Scholar
• Pyzer-Knapp EO, Simm GN, Aspuru Guzik A. A bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials. Mater Horiz. 2016;3:226–233.
   Web of Science ®Google Scholar
• Rongxin X, Sabre K. Quantum machine learning for electronic structure calculations. Nat Commun. 2018;9: 2041–1723.
   Web of Science ®Google Scholar
• Feng F, Lai L, Pei J. Computational chemical synthesis analysis and pathway design. Front Chem. 2018;6:199.
   PubMed Web of Science ®Google Scholar
• Inokuchi T, Li N, Morohoshi K, et al. Multiscale prediction of functional self-assembled materials using machine learning: high-performance surfactant molecules. Nanoscale. 2018;10:16013.
   PubMed Web of Science ®Google Scholar
• Yufeng H, Yalu C, Tao C, et al. Identification of the selective sites for electrochemical reduction of co to c2+ products on copper nanoparticles by combining reactive force fields, density functional theory, and machine learning. ACS Energy Lett. 2018;3:2983–2988.
   Web of Science ®Google Scholar
• de Gennes PG, Prost J. The physics of liquid crystals. Oxford, UK: Oxford University Press; 1995.
   Google Scholar
• White TJ, Broer DJ. Programmable and adaptive mechanics with liquid crystal polymer networks and elastomers. Nat Mater. 2015;14:1087–1098.
   PubMed Web of Science ®Google Scholar
• Ware TH, McConney ME, Wie JJ, et al. Voxelated liquid crystal elastomers. Science. 2015;347:982–984.
   PubMed Web of Science ®Google Scholar
• Humar M. Liquid-crystal-droplet optical microcavities. Liq Cryst. 2016;15:1–14.
   Google Scholar
• Han Y, Hen Z, Cao D, et al. Side-polished fiber as a sensor for the determination of nematic liquid crystal orientation. Sens Actuators B Chem. 2014;196:663–669.
   Web of Science ®Google Scholar
• Zheng Z, Li Y, Bisoyi HK, et al. Three-dimensional control of the helical axis of a chiral nematic liquid crystal by light. Nature. 2016;531:352–356.
   PubMed Web of Science ®Google Scholar
• Barón M. Definitions of basic terms relating to low-molar-mass and polymer liquid crystals. Pure Appl Chem. 2001;73:845–895.
   Web of Science ®Google Scholar
• Nowicka K, Dardas D, Kuczynski W, et al. Director distribution and surface anchoring potential in grandjean-cano wedge. Liq Cryst. 2014;41:10.
   Web of Science ®Google Scholar
• Rudzki A, Zalewski S, Suchodolska B, et al. Phase behavior and dynamics of binary and multicomponent thioester liquid crystal mixtures. J Phys Chem B. 2017;121:273–286.
   PubMed Web of Science ®Google Scholar
• Bedjaoui-Alachaher L, Bensaid H, Gordin C, et al. Solubility effects of multicomponent liquid crystal blends towards poly (n-butyl-acrylate). Liq Cryst. 2011;38:1315–1320.
   Web of Science ®Google Scholar
• Lu S, Chien L. Carbon nanotube doped liquid crystal ocb cells: physical and electro-optical properties. Opt Express. 2008;16:12777–12785.
   PubMed Web of Science ®Google Scholar
• Moreno-Razo JA, Sambriski EJ, Abbott NL, et al. Liquid-crystal-mediated self-assembly at nanodroplet interfaces. Nature. 2012;485:86.
   PubMed Web of Science ®Google Scholar
• Inokuchi T, Arai N. Liquid-crystal ordering mediated by self-assembly of surfactant solution confined in nanodroplet: a dissipative particle dynamics study. Mol Simul. 2017;43:1218–1226.
   Web of Science ®Google Scholar
• Rahimi M, Roberts TF, Armas-Perez JC, et al. Nanoparticle self-assembly at the interface of liquid crystal droplets. Proc Natl Acad Sci USA. 2015;112:5297–5302.
   PubMed Web of Science ®Google Scholar
• Lorieau J, Yao L, Bax A. Liquid crystalline phase of g-tetrad dna for nmr study of detergent-solubilized proteins. J AM Chem Soc. 2008;130:7536–7537.
   PubMed Web of Science ®Google Scholar
• Wen X, Meyer R, Casper D. Observation of smectic-a ordering in a solution of rigid-rod-like particles. Phys Rev Lett. 1989;63:2760.
   PubMed Web of Science ®Google Scholar
• Dogic Z, Fraden S. Smectic phase in a colloidal suspension of semiflexible virus particles. Phys Rev Lett. 1997;78:2417.
   Web of Science ®Google Scholar
• van der Kooji F, Kassapidou K, Lekkerkerker H. Liquid crystal phase transitions in suspensions of polydisperse plate-like particles. Nature. 2000;406:868.
   PubMed Web of Science ®Google Scholar
• Livolant F, Bouligand Y. Ordered phases of dna in vivo and in vitro. J Phys. 1986;47:1813.
   Web of Science ®Google Scholar
• Strzelecka T, Davidson M, Rill R. The highly concentrated liquid-crystalline phase of dna is columnar hexagonal. Nature. 1988;331:457.
   PubMed Web of Science ®Google Scholar
• Sied MB, Diez S, Salud J, et al. Liquid crystal binary mixtures of 8ocb + 10ocb. evidence for a smectic a-to-nematic tricritical point. J Phys Chem B. 2005;109:16284–16289.
   PubMed Web of Science ®Google Scholar
• Kapernaum N, Knecht F, CS H, et al. Formation of smectic phases in binary liquid crystal mixtures with a huge length ratio. Beilstein J Org Chem. 2012;8:1118–1125.
   PubMed Web of Science ®Google Scholar
• Hoogerbrugge P, Koelman J. Simulating microscopic hydro- dynamic phenomena with dissipative particle dynamics. EPL. 1992;19:155–160.
   Web of Science ®Google Scholar
• Espanõl P, Warren PB. Statical-mechanics of dissipative particle dynamics. EPL. 1995;30:191–196.
   Web of Science ®Google Scholar
• Groot RD, Warren PB. Dissipative particle dynamics: bridging the gap between atomistic and mesoscopic simulation. J Chem Phys. 1997;107:4423–4435.
   Web of Science ®Google Scholar
• Espaõl P, Warren PB. Perspective: dissipative particle dynamics. J Chem Phys. 2017;146:150901.
   PubMed Web of Science ®Google Scholar
• Schiller UD, Krüger T, Henrich O. Mesoscopic modelling and simulation of soft matter. Soft Matter. 2017;14:1.
   Web of Science ®Google Scholar
• Wang Y, Li Z, Xu J, et al. Concurrent coupling of atomistic simulation and mesoscopic hydrodynamics for flows over soft multi-functional surfaces. Soft Matter. 2019;15:1747–1757.
   PubMed Web of Science ®Google Scholar
• Luo Z, Jiang J. Molecular dynamics and dissipative particle dynamics simulations for the miscibility of poly(ethylene oxide)/poly(vinyl chloride) blends. Polym J. 2009;51:291–299.
   Web of Science ®Google Scholar
• Spaeth JR, Kevrekidis IG, Panagiotopoulos AZ. Dissipative particle dynamics simulations of polymer-protected nanoparticle self-assembly. J Chem Phys. 2011;135:184903.
   PubMed Web of Science ®Google Scholar
• Chunmiao D, Yujin J, Junwei X, et al. Morphology and performance of polymer solar cell characterized by DPD simulation and graph theory. Sci Rep. 2015;5:16854.
   PubMed Web of Science ®Google Scholar
• Šindelka K, Limpouchová Z, Procházka K. Computer study of the solubilization of polymer chains in polyelectrolyte complex cores of polymeric nanoparticles in aqueous media. Phys Chem Chem Phys. 2018;20:29876–29888.
   PubMed Web of Science ®Google Scholar
• Doi H, Okuwaki K, Mochizuki Y, et al. Dissipative particle dynamics (DPD) simulations with fragment molecular orbital (FMO) based effective parameters for 1-palmitoyl-2-oleoyl phosphatidyl choline (POPC) membrane. Chem Phys Lett. 2017;16:427–432.
   Web of Science ®Google Scholar
• Inokuchi T, Arai N. Relationship between water permeation and flip-flop motion in a bilayer membrane. Phys Chem Chem Phys. 2018;20:28155–28161.
   PubMed Web of Science ®Google Scholar
• Wang Y, Li B, Zhou Y, et al. Dissipative particle dynamics simulation study on the mechanisms of self-assembly of large multimolecular micelles from amphiphilic dendritic multiarm copolymers. Soft Matter. 2013;9:3293–3304.
   Web of Science ®Google Scholar
• Tan H, Yu C, Lu Z, et al. A dissipative particle dynamics simulation study on phase diagrams for the self-assembly of amphiphilic hyperbranched multiarm copolymers in various solvents. Soft Matter. 2017;13:6178–6188.
   PubMed Web of Science ®Google Scholar
• Zhou B, Luo W, Yang J, et al. Simulation of dispersion and alignment of carbon nanotubes in polymer flow using dissipative particle dynamics. Comput Mater Sci. 2017;126:35–42.
   Web of Science ®Google Scholar
• Kobayashi Y, Arai N. Self-assembly and viscosity behavior of janus nanoparticles in nanotube flow. Langmuir. 2017;33:736–743.
   PubMed Web of Science ®Google Scholar
• Zhang Z, Guo H. The phase behavior, structure, and dynamics of rodlike mesogens with various flexibility using dissipative particle dynamics simulation. J Chem Phys. 2010;133:144911.
   PubMed Web of Science ®Google Scholar
• Tsujinoue H, Inokuchi T, Arai N. Polymorphic transitions mediated by surfactants in liquid crystal nanodroplet. Liq Cryst. 2019;46:1428–1439.
   PubMed Web of Science ®Google Scholar
• Zheng X. One order parameter tensor mean field theory for biaxial liquid crystals. Discrete Continuous Dyn Syst Ser B. 2011;15:475–490.
   Web of Science ®Google Scholar
• Pleiner H, Liu M, Brand HR. Dynamics of the orientational tensor order parameter in polymeric systems. Inorg Chim Acta. 2002;41:375–382.
   Google Scholar
• Zhao T, Wang X. Solvent effect on phase transition of lyotropic rigid-chain liquid crystal polymer studied by dissipative particle dynamics. J Chem Phys. 2013;138:024910.
   PubMed Web of Science ®Google Scholar
• Mitchell T. Machine learning (mcgraw-hill series in computer science). New York: McGraw-Hill Education; 1997.
   Google Scholar
• Raccuglia P, Elbert K, Adler P, et al. Machine-learning-assisted materials discovery using failed experiments. Nature. 2016;533:73–77.
   PubMed Web of Science ®Google Scholar
• Breiman L. Random forests. Mach Learn. 2001;45:5–32.
   Web of Science ®Google Scholar
• Schneider A, Math D, Hommel G, et al. Linear regression analysis. Dtsch Arztebl Int. 2010;107:776–782.
   PubMed Web of Science ®Google Scholar
• Marquardt DW, Snee RD. Ridge regression in practice. Am Stat. 1975;29:3–20.
   Web of Science ®Google Scholar
• Mol CD, Vito ED, Rosasco L. Elastic-net regularization in learning theory. J Complexity. 2009;25:201–230.
   Web of Science ®Google Scholar
• Chang CC, Lin CJ. Training v-support vector regression: theory and algorithms. Neural Comput. 2002;14:1959–1977.
   PubMed Web of Science ®Google Scholar
• Alsahaf A, Azzopardi G, Ducro B, et al. Prediction of slaughter age in pigs and assessment of the predictive value of phenotypic and genetic information using random forest. J Anim Sci. 2018;96:4935–4943.
   PubMed Web of Science ®Google Scholar
• Shirazi SA, Parvandeh S, McKinney BA, et al. Random forest regression prediction of solid particle erosion in elbows. Powder Technol. 2018;338:983–992.
   Web of Science ®Google Scholar
• Wang Z, Wang Y, Zeng R, et al. Random forest based hourly building energy prediction. Energy Build. 2018;171:11–25.
   Web of Science ®Google Scholar
• Zeng S, Li G, Zhao Y, et al. Machine learning-aided design of materials with target elastic properties. J Phys Chem C. 2019;123:5042–5047.
   PubMed Web of Science ®Google Scholar