Minimum-energy configurations of atomic clusters: new results obtained by simulated annealing

1987 ◽  
Vol 133 (5) ◽  
pp. 405-410 ◽  
Author(s):  
L.T. Wille
1994 ◽  
Vol 03 (04) ◽  
pp. 477-495 ◽  
Author(s):  
Terry J. Ligocki ◽  
James A. Sethian

This paper describes the development and implementation of an algorithm which uses simulated annealing to recognize knots by minimizing an energy function defined over all knots. A knot is represented by a piecewise linear curve and the vertices of this curve are perturbed using simulated annealing to minimize the energy. Moving one line segment through another line segment is prohibited. The resulting minimum energy configuration is defined to be the canonical form. The algorithm is then tested with two different types of energy over a collection of complex knots.


1979 ◽  
Vol 57 (5) ◽  
pp. 538-551 ◽  
Author(s):  
Peeter Kruus ◽  
Barbara E. Poppe

A model of solutions of alkali halides in DMSO is developed. Each ion is described by a radius, a charge, a polarizability, and an exponential repulsion parameter. Each molecule is described by a polarizability, charges, 6-12 energy parameters, and 6-12 distance parameters centered on each of the 10 atoms in the molecule. The model is applied to calculate (i) the vaporization energy of solvent molecules, (ii) single ion solvation energies and configurations of the solvating molecules, and (iii) the energy as a function of reaction coordinate for the formation of an ion pair. The energies and configurations are obtained by allowing the systems to relax to minimum energy configurations by allowing motion of the molecules. The results of (i) give a vaporization energy 60% of the experimental. The results of (ii) give solvation energies in reasonable agreement with the experimental, and configurations which are reasonable from the point of view of mobilities of ions. The results of (iii) show the presence of a distinct solvent separated ion pair which actually has an energy lower than the contact ion pair. Advantages and problems involved in using this approach to model solutions are discussed.


Soft Matter ◽  
2008 ◽  
Vol 4 (7) ◽  
pp. 1396 ◽  
Author(s):  
Gernot J. Pauschenwein ◽  
Gerhard Kahl

Nature ◽  
1986 ◽  
Vol 319 (6053) ◽  
pp. 454-454 ◽  
Author(s):  
M.G. CALKIN ◽  
D. KIANG ◽  
D.A. TINDALL

Author(s):  
SANGHAMITRA BANDYOPADHYAY ◽  
UJJWAL MAULIK ◽  
MALAY KUMAR PAKHIRA

An efficient partitional clustering technique, called SAKM-clustering, that integrates the power of simulated annealing for obtaining minimum energy configuration, and the searching capability of K-means algorithm is proposed in this article. The clustering methodology is used to search for appropriate clusters in multidimensional feature space such that a similarity metric of the resulting clusters is optimized. Data points are redistributed among the clusters probabilistically, so that points that are farther away from the cluster center have higher probabilities of migrating to other clusters than those which are closer to it. The superiority of the SAKM-clustering algorithm over the widely used K-means algorithm is extensively demonstrated for artificial and real life data sets.


1996 ◽  
Vol 64 (1) ◽  
pp. 157-174 ◽  
Author(s):  
Vittorio Murino ◽  
Carlo S. Regazzoni ◽  
Gian Luca Foresti

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