NUMERICAL STUDY OF QUANTUM DOT SPECTRUM CALCULATION ON THE BASE OF MONTE CARLO METHOD
( Pp. 74-79)
More about authors
Popov Alexander M.
doktor fiziko-matematicheskih nauk, professor; fakultet vychislitelnoy matematiki i kibernetiki
Lomonosov Moscow State University
Lomonosov Moscow State University
Abstract:
The work is directed to numerical simulation of quantum dots spectrum for molecular nanostructure of small size for creation of new nanotechnology. Quantum dots are the small peaces of semiconductor which presents the molecular system heterostucture. The cariers of charge are confined in small region. The main acsent is made on development of effective method for determination of eigenfuncions and eigenvalues of quantum dot. Quantum dots are used in nanoelectronics, in bio-sensors of nanosize, and in the systems of medical diagnostics of high precision.
How to Cite:
Popov A.M., (2019), NUMERICAL STUDY OF QUANTUM DOT SPECTRUM CALCULATION ON THE BASE OF MONTE CARLO METHOD. Computational Nanotechnology, 3 => 74-79.
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Kerr R.A., Bartol T.M. Fast Monte Carlo simulation methods for biological reaction-diffusion systems in solution and on surfaces. 2008.
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Riley M.R., Buettner H.M. Monte Carlo simulation of diffusion and reaction in two-dimensional cell structures // Biophysical Journal. May 1995. Vol. 68.
Goldman J., Andrews S., Bray D. Size and composition of membrane protein clusters predicted by Monte Carlo analysis. 2004.
Aluru S. Handbook of Computational Molecular Biology.
Lakhno V.D. Bioinformatika i vysokoproizvodiel nye vychisleniya. Institut matematicheskikh problem biologii RAN.
Wang Y. Open MP and MPI implementation of diffusion Monte Carlo algorithm.
Komp yutery i superkomp yutery v biologii / pod red. V.D. Lakhno, M.N. Ustinina. M.-Izhevsk: In-t komp yuternykh issledovaniy, 2002.
Hansmann U.H.E. Parallel tempering algorithm for conformational studies of biological molecules. 1997.
Earl D.J., Deem M.W. Parallel tempering: Theory, applications, and new perspectives, 2005.
Caflisch A., Niederer P., Anliker M. Monte Carlo minimization with thermalization for global optimization of polypeptide conformations in Cartesian coordinate space. 1992.
Sanbonmatsu K.Y., Garcia A.E. Structure of met-enkephalin in explicit aqueous solution using replica exchange molecular dynamics, 2002.
Zimmerman S.S., Pottle M.S., Nemethy G., Scheraga H.A. Conformational analysis of the 20 naturally occurring amino acid residues using ECEPP // Macromolecules. 1977. Vol. 10. № 1.
Nemethy G., Pottle M.S., Scheraga H.A. Energy parameters in polypeptides. 9. Updating of geometrical parameters, nonbonded interactions, and hydrogen bond for the naturally occurring amino acids // J. Phys. Chem. 1983. № 87.
Sippl M.J., Nemethy G., Scheraga H.A. Intermolecular potentials from crystal data. 6. Determination of empirical potentials for O-H-O-C hydrogen bonds from packing configurations // J. Phys. Chem. 1984. № 88.
McGuire R.F., Vanderkool G. Determination of intermolecular potentials from crystal data. II. Crystal packing with applications to poly (amino acids). 1970.
Zaman M.H., Shen M. Computer simulation of met-enkephalin using explicit atom and united atom potentials: similarities, differences, and suggestions for improvement // J. Phys. Chem. 2003. № 107.
Rackovsky S., Scheraga H.A. Differential geometry and polymer conformation. 4. Conformational and nucleation properties of individual. amino acids, macromolecules. 1982. R. 15.
Hsu H., Berg B.A., Grassberger P. Monte Carlo protein folding: Simulations of met-enkephalin with solvent-accessible area parametrizations, 2004.
Mitas L. Diffusion Monte Carlo, national center for supercomputing applications. University of Illinois at Urbana-Champaign. Altekar G.et al. Parallel metropolis coupled Markov chain Monte Carlo for bayesian phylogenetic inference // Bioinformatics. 2004. № 20 (3). R. 407-415.
Geyer C.J. Markov chain Monte Carlo maximum likelihood // Computing Science and Statistics. 1991. № 23. R. 156-163,
