{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90714"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90714","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Database optimization algorithm for empirical potentials","abstract":"The development for accurate and efficient empirical potential models requires years of efforts and is highly intuitional. This study provides an automated, quantitative algorithm to find the optimal empirical potential model for a pre-determined testing set of desired structure properties. We employ Bayesian sampling technique to estimate the errors for the structural property functions in the testing set. We provide the first analytical derivations of how modifications in the fitting database affect the testing set errors. A new binary modified embedded-atom method functional form is developed for Ti-O interactions where O is in the dilute limit. The optimal Ti-O potential are tested against a variety of structure properties to verify the transferability of the potential. We propose and optimize two types of objective functions which measures the transferability in the testing set. One aims to minimize the relative errors of different fitting databases for the testing set, and the other uses the logistic function in classification regression analysis to categorize the prediction errors in the testing set into good and bad ones. We develop a parallelized genetic algorithm to efficiently evaluate the objective function and perform global search for the optimal empirical potential model.","abstract_html":"The development for accurate and efficient empirical potential models requires years of efforts and is highly intuitional. This study provides an automated, quantitative algorithm to find the optimal empirical potential model for a pre-determined testing set of desired structure properties. We employ Bayesian sampling technique to estimate the errors for the structural property functions in the testing set. We provide the first analytical derivations of how modifications in the fitting database affect the testing set errors. A new binary modified embedded-atom method functional form is developed for Ti-O interactions where O is in the dilute limit. The optimal Ti-O potential are tested against a variety of structure properties to verify the transferability of the potential. We propose and optimize two types of objective functions which measures the transferability in the testing set. One aims to minimize the relative errors of different fitting databases for the testing set, and the other uses the logistic function in classification regression analysis to categorize the prediction errors in the testing set into good and bad ones. We develop a parallelized genetic algorithm to efficiently evaluate the objective function and perform global search for the optimal empirical potential model.","abstract_has_math":false,"creators":["Zhang, Pinchao"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Materials Science & Engr","degree_department":null,"school":null,"contributors":["Trinkle, Dallas R.","Bellon, Pascal","Chen, Yuguo","Ferguson, Andrew"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T20:26:45Z","date_published":"2016-07-07T20:26:45Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Empirical potential models","Optimization algorithm","Bayesian statistics","Monte Carlo","Genetic algorithm","Titanium oxygen interaction"],"languages":["en"],"rights":["Copyright 2016 Pinchao Zhang"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90714","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Trinkle, Dallas R.","Bellon, Pascal","Chen, Yuguo","Ferguson, Andrew"]},{"key":"dc:creator","label":"Author","values":["Zhang, Pinchao"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T20:26:45Z","2018-07-08T09:15:36Z","2016-03-01","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Materials Science & Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Empirical potential models","Optimization algorithm","Bayesian statistics","Monte Carlo","Genetic algorithm","Titanium oxygen interaction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Pinchao Zhang"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90714"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The development for accurate and efficient empirical potential models requires years of efforts and is highly intuitional. This study provides an automated, quantitative algorithm to find the optimal empirical potential model for a pre-determined testing set of desired structure properties. We employ Bayesian sampling technique to estimate the errors for the structural property functions in the testing set. We provide the first analytical derivations of how modifications in the fitting database affect the testing set errors. A new binary modified embedded-atom method functional form is developed for Ti-O interactions where O is in the dilute limit. The optimal Ti-O potential are tested against a variety of structure properties to verify the transferability of the potential. We propose and optimize two types of objective functions which measures the transferability in the testing set. One aims to minimize the relative errors of different fitting databases for the testing set, and the other uses the logistic function in classification regression analysis to categorize the prediction errors in the testing set into good and bad ones. We develop a parallelized genetic algorithm to efficiently evaluate the objective function and perform global search for the optimal empirical potential model.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Pinchao Zhang, accepted the attached license on 2016-02-29 at 19:44.","The student, Pinchao Zhang, submitted this Dissertation for approval on 2016-02-29 at 19:47.","This Dissertation was approved for publication on 2016-03-01 at 13:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9048 on 2016-07-07 at 13:48:10","Made available in DSpace on 2016-07-07T20:26:45Z (GMT). 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This study provides an automated, quantitative algorithm to find the optimal empirical potential model for a pre-determined testing set of desired structure properties. We employ Bayesian sampling technique to estimate the errors for the structural property functions in the testing set. We provide the first analytical derivations of how modifications in the fitting database affect the testing set errors. A new binary modified embedded-atom method functional form is developed for Ti-O interactions where O is in the dilute limit. The optimal Ti-O potential are tested against a variety of structure properties to verify the transferability of the potential. We propose and optimize two types of objective functions which measures the transferability in the testing set. One aims to minimize the relative errors of different fitting databases for the testing set, and the other uses the logistic function in classification regression analysis to categorize the prediction errors in the testing set into good and bad ones. We develop a parallelized genetic algorithm to efficiently evaluate the objective function and perform global search for the optimal empirical potential model.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-05-01","The student, Pinchao Zhang, accepted the attached license on 2016-02-29 at 19:44.","The student, Pinchao Zhang, submitted this Dissertation for approval on 2016-02-29 at 19:47.","This Dissertation was approved for publication on 2016-03-01 at 13:19.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9048 on 2016-07-07 at 13:48:10","Made available in DSpace on 2016-07-07T20:26:45Z (GMT). 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