COMPUTER MODELING THIN FILM GROWTH ON THE SURFACE BY LOW ENERGY CLUSTER DEPOSITION
( Pp. 160-163)

More about authors
Muminov Ramizulla A. Academician, Doct. Sci. (Phys. and Math.), Professor
Physical-Technical Institute of the SPA “Physics-Sun” of the Academy of Science of Uzbekistan
Tashkent, Republic of Uzbekistan Rasulov Akbarali Mahamatovich doktor fiziko-matematicheskih nauk, professor
Tashkent University of Information Technologies Ferghana branch Ibragimov Nodir Ikromjonovich starshiy prepodavatel
Ferghana Polytechnic Institute
Abstract:
A report is presented about progress in the understanding of the properties of bi-metallic nanoparticles, their interaction with surfaces subsequent to low energy slowing down and the properties of nanostructured materials formed with these particles. A nanoparticle contains from a few atoms for the smallest ones to several thousand for the largest ones considered here. The properties of an atom result from quantization and the same is true for the molecules they form. The same is thus true for the smallest nanoparticles. At the other edge, many of the properties of macroscopic materials are well described by a classical approach and nanoparticles appear as objects at the fringing field between quantum and classical behaviors. In the study of their properties, using either a quantum or a classical approach, atomic scale methods appear as naturally well-suited. Atoms are considered as individual objects interacting via their outer shell electrons only. However even with such an approximation, solving the Schrödinger equation becomes quickly prohibitively heavy as the number of atoms involved increases. For the heaviest elements, relativistic effects make the problem even heavier.
How to Cite:
Muminov R.A., Rasulov A.M., Ibragimov N.I., (2019), COMPUTER MODELING THIN FILM GROWTH ON THE SURFACE BY LOW ENERGY CLUSTER DEPOSITION. Computational Nanotechnology, 2 => 160-163.
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Keywords:
computer simulation, low energy, cluster, deposition, slowing down, Molecular Dynamics, parallelization, Embedded Atom Model.


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