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Modeling Chromophore Order: A Guide For Improving EO Performance

Published online by Cambridge University Press:  19 August 2014

Andreas F Tillack ,
Lewis E Johnson ,
Meghana Rawal ,
Larry R Dalton  and
Bruce H Robinson
Andreas F Tillack
Affiliation:
Department of Chemistry, University of Washington, Box 351700, Seattle, WA 98195, U.S.A.
Lewis E Johnson
Affiliation:
Department of Chemistry, University of Washington, Box 351700, Seattle, WA 98195, U.S.A. Department of Chemistry, Pomona College, 645 N. College Ave, Claremont, CA 91711, U.S.A.
Meghana Rawal
Affiliation:
Department of Chemistry, University of Washington, Box 351700, Seattle, WA 98195, U.S.A.
Larry R Dalton
Affiliation:
Department of Chemistry, University of Washington, Box 351700, Seattle, WA 98195, U.S.A.
Bruce H Robinson
Affiliation:
Department of Chemistry, University of Washington, Box 351700, Seattle, WA 98195, U.S.A.
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Abstract

The use of organic nonlinear optical (ONLO) materials in electro-optic (EO) modulators requires that the active molecular components (chromophores) be acentrically oriented. The fundamental molecular constituents are in a condensed, glassy phase. Molecular orientation in such systems is typically achieved by applying a DC poling field to the glassy material. We are developing efficient coarse-grained classical Monte Carlo (MC) methods to simulate the order of such systems. The most challenging aspects of these simulations are convergence to an experimentally relevant equilibrium ensemble and verification of simulation accuracy. We use a variety of molecular descriptions and a variety of MC methods to achieve proper order in the shortest number of computational cycles possible. Herein, we illustrate a few examples of the types of calculations and compare with experimental results with representative amorphous organic materials, including electro-optic chromophores.

Keywords

simulationopticalpolymer
Type
Articles
Information
MRS Online Proceedings Library (OPL) , Volume 1698: Symposium II/JJ/KK – Materials, Processes and Devices for Nanophotonics, Nonlinear Optics and Resonant Optics , 2014 , mrss14-1698-jj08-05
Copyright
Copyright © Materials Research Society 2014 

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References

REFERENCES

Allen, M.P. and Tildesley, D.J., Computer Simulation of Liquids (Clarendon Press ; Oxford University Press, Oxford [England]; New York, 1989).Google Scholar
Peter, C. and Kremer, K., Soft Matter 5, 4357 (2009).Google Scholar
Marrink, S.J., Risselada, H.J., Yefimov, S., Tieleman, D.P., and de Vries, A.H., J. Phys. Chem. B 111, 7812 (2007).CrossRefGoogle Scholar
Izvekov, S. and Voth, G.A., J. Chem. Phys. 123, 134105 (2005).CrossRefGoogle Scholar
Huang, L. and Roux, B., J. Chem. Theory Comput. 9, 3543 (2013).CrossRefGoogle Scholar
Mladek, B.M., Fornleitner, J., Martinez-Veracoechea, F.J., Dawid, A., and Frenkel, D., Soft Matter 9, 7342 (2013).CrossRefGoogle Scholar
Johnson, L.E., Barnes, R., Draxler, T.W., Eichinger, B.E., and Robinson, B.H., J. Phys. Chem. B 114, 8431 (2010).Google Scholar
Benight, S.J., Johnson, L.E., Barnes, R., Olbricht, B.C., Bale, D.H., Reid, P.J., Eichinger, B.E., Dalton, L.R., Sullivan, P.A., and Robinson, B.H., J. Phys. Chem. B 114, 11949 (2010).CrossRefGoogle Scholar
Gay, J.G. and Berne, B.J., J. Chem. Phys. 74, 3316 (1981).CrossRefGoogle Scholar
Perram, J.W. and Wertheim, M.S., J. Comput. Phys. 58, 409 (1985).Google Scholar
Zhang, C., Dalton, L.R., Oh, M.-C., Zhang, H., and Steier, W.H., Chem. Mater. 13, 3043 (2001).CrossRefGoogle Scholar
Jorgensen, W.L., Maxwell, D.S., and Tirado-Rives, J., J. Am. Chem. Soc. 118, 11225 (1996).Google Scholar
Chernyak, Y., J. Chem. Eng. Data 51, 416 (2006).CrossRefGoogle Scholar