Optimization of transition state structures using genetic algorithms
Thesis (M.Sc.)--Memorial University of Newfoundland, 2000. Computational Science Bibliography: leaves 80-82 Geometry optimization has long been an active research area in theoretical chemistry. Many algorithms currently exist for the optimization of minima (reactants, intermediates, and products) on...
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ftmemorialunivdc:oai:collections.mun.ca:theses3/72523 2023-05-15T17:23:32+02:00 Optimization of transition state structures using genetic algorithms Bungay, Sharene D., 1976- Memorial University of Newfoundland.Computational Science Programme 2000 x, 104 leaves Image/jpeg; Application/pdf http://collections.mun.ca/cdm/ref/collection/theses3/id/72523 eng eng Electronic Theses and Dissertations (25.08 MB) -- http://collections.mun.ca/PDFs/theses/Bungay_ShareneD.pdf a1492123 http://collections.mun.ca/cdm/ref/collection/theses3/id/72523 The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries Mathematical optimization Genetic algorithms Text Electron ic thesis or dissertation 2000 ftmemorialunivdc 2015-08-06T19:18:11Z Thesis (M.Sc.)--Memorial University of Newfoundland, 2000. Computational Science Bibliography: leaves 80-82 Geometry optimization has long been an active research area in theoretical chemistry. Many algorithms currently exist for the optimization of minima (reactants, intermediates, and products) on a potential energy surface. However, determination of transition state structures (first order saddle points) has been an ongoing problem. The computational technique of genetic algorithms has recently been applied to optimization problems in many disciplines. Genetic algorithms are a type of evolutionary computing in which a population of individuals, whose genes collectively encode candidate solutions to the problem being solved, evolve toward a desired objective. Each generation is biased towards producing individuals which closely resemble the known desired features of the optimum. This thesis contains a discussion of existing techniques for geometry optimization, a description of genetic algorithms, and an explanation of how the genetic algorithm technique was applied to transition state optimization and incorporated into the existing ah initio package Mungauss. Results from optimizing mathematical functions, demonstrating the effectiveness of the genetic algorithm implemented to optimize first order saddle points, are presented, followed by results from the optimization of standard chemical structures used for the testing of transition state optimization methods. Finally, some ideas for future method modifications to increase the efficiency of the genetic algorithm implementation used are discussed. Text Newfoundland studies University of Newfoundland Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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Memorial University of Newfoundland: Digital Archives Initiative (DAI) |
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Mathematical optimization Genetic algorithms |
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Mathematical optimization Genetic algorithms Bungay, Sharene D., 1976- Optimization of transition state structures using genetic algorithms |
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Mathematical optimization Genetic algorithms |
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Thesis (M.Sc.)--Memorial University of Newfoundland, 2000. Computational Science Bibliography: leaves 80-82 Geometry optimization has long been an active research area in theoretical chemistry. Many algorithms currently exist for the optimization of minima (reactants, intermediates, and products) on a potential energy surface. However, determination of transition state structures (first order saddle points) has been an ongoing problem. The computational technique of genetic algorithms has recently been applied to optimization problems in many disciplines. Genetic algorithms are a type of evolutionary computing in which a population of individuals, whose genes collectively encode candidate solutions to the problem being solved, evolve toward a desired objective. Each generation is biased towards producing individuals which closely resemble the known desired features of the optimum. This thesis contains a discussion of existing techniques for geometry optimization, a description of genetic algorithms, and an explanation of how the genetic algorithm technique was applied to transition state optimization and incorporated into the existing ah initio package Mungauss. Results from optimizing mathematical functions, demonstrating the effectiveness of the genetic algorithm implemented to optimize first order saddle points, are presented, followed by results from the optimization of standard chemical structures used for the testing of transition state optimization methods. Finally, some ideas for future method modifications to increase the efficiency of the genetic algorithm implementation used are discussed. |
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Memorial University of Newfoundland.Computational Science Programme |
format |
Text |
author |
Bungay, Sharene D., 1976- |
author_facet |
Bungay, Sharene D., 1976- |
author_sort |
Bungay, Sharene D., 1976- |
title |
Optimization of transition state structures using genetic algorithms |
title_short |
Optimization of transition state structures using genetic algorithms |
title_full |
Optimization of transition state structures using genetic algorithms |
title_fullStr |
Optimization of transition state structures using genetic algorithms |
title_full_unstemmed |
Optimization of transition state structures using genetic algorithms |
title_sort |
optimization of transition state structures using genetic algorithms |
publishDate |
2000 |
url |
http://collections.mun.ca/cdm/ref/collection/theses3/id/72523 |
genre |
Newfoundland studies University of Newfoundland |
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Newfoundland studies University of Newfoundland |
op_source |
Paper copy kept in the Centre for Newfoundland Studies, Memorial University Libraries |
op_relation |
Electronic Theses and Dissertations (25.08 MB) -- http://collections.mun.ca/PDFs/theses/Bungay_ShareneD.pdf a1492123 http://collections.mun.ca/cdm/ref/collection/theses3/id/72523 |
op_rights |
The author retains copyright ownership and moral rights in this thesis. Neither the thesis nor substantial extracts from it may be printed or otherwise reproduced without the author's permission. |
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