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Cellular Automaton Simulation of Polymers

Published online by Cambridge University Press:  25 February 2011

M. A. Smith ,
Y. Bar-Yam ,
Y. Rabin ,
N. Margolus ,
T. Toffoli  and
C. H. Bennett
M. A. Smith
Affiliation:
MIT Laboratory for Computer Science, Cambridge, MA 02139
Y. Bar-Yam
Affiliation:
ECS, 44 Cummington St., Boston University, Boston MA 02215
Y. Rabin
Affiliation:
Department of Physics, Bar-Ilan University, Ramat-Gan 52900, Israel
N. Margolus
Affiliation:
MIT Laboratory for Computer Science, Cambridge, MA 02139
T. Toffoli
Affiliation:
MIT Laboratory for Computer Science, Cambridge, MA 02139
C. H. Bennett
Affiliation:
IBM T. J. Watson Res. Ctr., Yorktown Heights, NY 11973
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Abstract

In order to improve our ability to simulate the complex behavior of polymers, we introduce dynamical models in the class of Cellular Automata (CA). Space partitioning methods enable us to overcome fundamental obstacles to large scale simulation of connected chains with excluded volume by parallel processing computers. A highly efficient, two-space algorithm is devised and tested on both Cellular Automata Machines (CAMs) and serial computers. Preliminary results on the static and dynamic properties of polymers in two dimensions are reported.

Type
Research Article
Information
MRS Online Proceedings Library (OPL) , Volume 248: Symposium O – Complex Fluids , 1991 , 483
Copyright
Copyright © Materials Research Society 1992

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References

References:

1 deGennes, P. G. “Scaling Concepts in Polymer Physics” (Cornell Univ. Press, Ithaca 1979)Google Scholar
2 Doi, M. and Edwards, S. F. “Theory of Polymer Dynamics” (Oxford Science Publications, Oxford 1986)Google Scholar
3 McCammon, J. A. and Harvey, S. C. “Dynamics of Proteins and Nucleic Acids” (Cambridge Univ. Press, Cambridge 1987)Google Scholar
4 “Monte-Carlo Methods in Statistical Physics”, 2nd ed., Binder, K. ed. (Springer-Verlag, Berlin 1986),”Applications of Monte-Carlo Methods in Statistical Physics”, 2nd ed., K. Binder ed. (Springer-Verlag, Berlin 1987)CrossRefGoogle Scholar
5 Boghosian, B. M., Computers in Physics 4, 14 (1990)CrossRefGoogle Scholar
6 “Theory and Applications of Cellular Automata”, Wolfram, S. Ed.,(World Scientific, Singapore, 1986)Google Scholar
7 Gardner, M., Mathematical Games, Sci. Amer. (Feb. Mar., Apr. 1971; Jan. 1972)Google Scholar
8 “Cellular Automata”, Farmer, D., Toffoli, T. and Wolfram, S. (eds), (North-Holland, 1984), or T. Toffoli, Cellular Automaton mechanics”, PhD Thesis, Logic of Computers Group, U. of Michigan (1977)Google Scholar
9 Toffoli, T. and Margolus, N., “Cellular Automata Machines: A New Environment for Modeling”, (MIT Press, 1987)Google Scholar
10 Chopard, B., J. of Phys. A 23, 1671 (1990)CrossRefGoogle Scholar
11 Koelman, J. M. V. A., Phys. Rev. Lett. 64, 1915 (1990)CrossRefGoogle Scholar
12 Carmesin, I. and Kremer, K., Macromolecules 21, 2819 (1988)Google Scholar
13 Wittmann, H. P. and Kremer, K., Computer Physics Communications 61, 309 (1990)CrossRefGoogle Scholar
14 Kremer, K. and Grest, G. S., J. Chem. Phys. 92, 5057 (1990)CrossRefGoogle Scholar