{"id":{"repo_id":"usf","oai_identifier":"oai:digitalcommons.usf.edu:etd-6813"},"canonical_url":"https://search.dev.ndltd.org/etd/usf/oai:digitalcommons.usf.edu:etd-6813","repository":{"repo_id":"usf","name":"University of South Florida","base_url":"https://digitalcommons.usf.edu/do/oai/"},"display":{"title":"High Dimensional Non-Linear Optimization of Molecular Models","abstract":"Molecular models allow computer simulations to predict the microscopic properties of macroscopic systems. Molecular modeling can also provide a fully understood test system for the application of theoretical methods. The power of a model lies in the accuracy of the parameter values which govern its mathematical behavior. In this work, a new software, called ParOpt, for general high dimensional non-linear optimization will be presented. The software provides a very general framework for the optimization of a wide variety of parameter sets. The software is especially powerful when applied to the difficult task of molecular model parameter optimization. Three applications of the ParOpt software, and the Nelder-Mead algorithm implemented within it, are presented: a coarse-grained (CG) water--ion model, a model for the determination of lipid bilayer structure via the interpretation of scattering data, and a reactive molecular dynamics (ReaxFF) model for oxygen and hydrogen. Each problem presents specific difficulties. The power and generality of the ParOpt software is illustrated by the successful optimization of such a diverse set of problems.","abstract_html":"Molecular models allow computer simulations to predict the microscopic properties of macroscopic systems. Molecular modeling can also provide a fully understood test system for the application of theoretical methods. The power of a model lies in the accuracy of the parameter values which govern its mathematical behavior. In this work, a new software, called ParOpt, for general high dimensional non-linear optimization will be presented. The software provides a very general framework for the optimization of a wide variety of parameter sets. The software is especially powerful when applied to the difficult task of molecular model parameter optimization. Three applications of the ParOpt software, and the Nelder-Mead algorithm implemented within it, are presented: a coarse-grained (CG) water--ion model, a model for the determination of lipid bilayer structure via the interpretation of scattering data, and a reactive molecular dynamics (ReaxFF) model for oxygen and hydrogen. Each problem presents specific difficulties. The power and generality of the ParOpt software is illustrated by the successful optimization of such a diverse set of problems.","abstract_has_math":false,"creators":["Fogarty, Joseph C."],"institution":"Digital Commons @ University of South Florida","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-11-20T08:00:00Z","date_published":"2014-11-20T08:00:00Z","updated_at":"2026-07-24T05:42:11Z","subjects":["Molecular Modeling","Optimization","Simulation","Physics"],"languages":[],"rights":["default"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.usf.edu/etd/5618","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Fogarty, Joseph C."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-11-20T08:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["Digital Commons @ University of South Florida"]},{"key":"dc:type","label":"Dc Type","values":["dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Molecular Modeling","Optimization","Simulation","Physics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["default"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.usf.edu/etd/5618","https://digitalcommons.usf.edu/context/etd/article/6813/viewcontent/Fogarty_usf_0206D_12668.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Molecular models allow computer simulations to predict the microscopic properties of macroscopic systems. Molecular modeling can also provide a fully understood test system for the application of theoretical methods. The power of a model lies in the accuracy of the parameter values which govern its mathematical behavior. In this work, a new software, called ParOpt, for general high dimensional non-linear optimization will be presented. The software provides a very general framework for the optimization of a wide variety of parameter sets. The software is especially powerful when applied to the difficult task of molecular model parameter optimization. Three applications of the ParOpt software, and the Nelder-Mead algorithm implemented within it, are presented: a coarse-grained (CG) water--ion model, a model for the determination of lipid bilayer structure via the interpretation of scattering data, and a reactive molecular dynamics (ReaxFF) model for oxygen and hydrogen. Each problem presents specific difficulties. The power and generality of the ParOpt software is illustrated by the successful optimization of such a diverse set of problems."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:source","label":"Dc Source","values":["USF Tampa Graduate Theses and Dissertations"]},{"key":"dc:title","label":"Title","values":["High Dimensional Non-Linear Optimization of Molecular Models"]}]}],"canonical_facts":{"dc:creator":["Fogarty, Joseph C."],"dc:date":["2014-11-20T08:00:00Z"],"dc:description":["Molecular models allow computer simulations to predict the microscopic properties of macroscopic systems. Molecular modeling can also provide a fully understood test system for the application of theoretical methods. The power of a model lies in the accuracy of the parameter values which govern its mathematical behavior. In this work, a new software, called ParOpt, for general high dimensional non-linear optimization will be presented. The software provides a very general framework for the optimization of a wide variety of parameter sets. The software is especially powerful when applied to the difficult task of molecular model parameter optimization. Three applications of the ParOpt software, and the Nelder-Mead algorithm implemented within it, are presented: a coarse-grained (CG) water--ion model, a model for the determination of lipid bilayer structure via the interpretation of scattering data, and a reactive molecular dynamics (ReaxFF) model for oxygen and hydrogen. Each problem presents specific difficulties. The power and generality of the ParOpt software is illustrated by the successful optimization of such a diverse set of problems."],"dc:format":["application/pdf"],"dc:identifier":["https://digitalcommons.usf.edu/etd/5618","https://digitalcommons.usf.edu/context/etd/article/6813/viewcontent/Fogarty_usf_0206D_12668.pdf"],"dc:publisher":["Digital Commons @ University of South Florida"],"dc:rights":["default"],"dc:source":["USF Tampa Graduate Theses and Dissertations"],"dc:subject":["Molecular Modeling","Optimization","Simulation","Physics"],"dc:title":["High Dimensional Non-Linear Optimization of Molecular Models"],"dc:type":["dissertation"]},"updated_at":"2026-07-24T05:42:11Z"}