A genetic algorithm approach in interface and surface structure optimization
Author(s) -
Jian Zhang
Publication year - 2010
Language(s) - English
Resource type - Reports
DOI - 10.2172/985315
Subject(s) - genetic algorithm , interface (matter) , surface (topology) , algorithm , tungsten , thermal , computer science , physics , materials science , mathematics , geometry , machine learning , bubble , maximum bubble pressure method , parallel computing , meteorology , metallurgy
The thesis is divided into two parts. In the first part a global optimization method is developed for the interface and surface structures optimization. Two prototype systems are chosen to be studied. One is Si[001] symmetric tilted grain boundaries and the other is Ag/Au induced Si(111) surface. It is found that Genetic Algorithm is very efficient in finding lowest energy structures in both cases. Not only existing structures in the experiments can be reproduced, but also many new structures can be predicted using Genetic Algorithm. Thus it is shown that Genetic Algorithm is a extremely powerful tool for the material structures predictions. The second part of the thesis is devoted to the explanation of an experimental observation of thermal radiation from three-dimensional tungsten photonic crystal structures. The experimental results seems astounding and confusing, yet the theoretical models in the paper revealed the physics insight behind the phenomena and can well reproduced the experimental results.
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