{"id":{"repo_id":"gatech","oai_identifier":"oai:repository.gatech.edu:1853/60125"},"canonical_url":"https://search.dev.ndltd.org/etd/gatech/oai:repository.gatech.edu:1853/60125","repository":{"repo_id":"gatech","name":"Georgia Tech","base_url":"https://repository.gatech.edu/server/oai/request"},"display":{"title":"Data-driven PSP linkages for atomistic datasets","abstract":"For a variety of materials, atomic-scale modeling techniques are commonly employed as a means of investigating fundamental properties, including both structural and chemical responses. While force-field based calculations are significantly less computationally expensive than their quantum-mechanical counterparts, the datasets often investigated are large in size (10^3 – 10^9 atoms) and high-dimensional, and thus cumbersome for use in multi-scale models. The development of quantitative “process-structure-property” (PSP) linkages for atomistic simulations presents a powerful route to convert atomistic simulation data into actionable knowledge. Here, a framework is presented for quantifying structure from these simulations in full- and reduced-dimensional form, and a series of protocols are developed for establishing regression models for process-structure and structure-property linkages.","abstract_html":"For a variety of materials, atomic-scale modeling techniques are commonly employed as a means of investigating fundamental properties, including both structural and chemical responses. While force-field based calculations are significantly less computationally expensive than their quantum-mechanical counterparts, the datasets often investigated are large in size (10^3 – 10^9 atoms) and high-dimensional, and thus cumbersome for use in multi-scale models. The development of quantitative “process-structure-property” (PSP) linkages for atomistic simulations presents a powerful route to convert atomistic simulation data into actionable knowledge. Here, a framework is presented for quantifying structure from these simulations in full- and reduced-dimensional form, and a series of protocols are developed for establishing regression models for process-structure and structure-property linkages.","abstract_has_math":false,"creators":["Gomberg, Joshua A."],"institution":"Georgia Institute of Technology","degree_name":null,"degree_level":"Doctoral","degree_discipline":null,"degree_department":"Materials Science and Engineering","school":null,"contributors":[],"advisors":["Kalidindi, Surya R."],"committee_chairs":[],"committee_members":["McDowell, David L.","Li, Mo","Haaland, Benjamin","Garmestani, Hamid"],"year":2017,"date_issued":"2017-05-23","date_published":"2017-05-23","updated_at":"2026-07-27T19:50:24Z","subjects":["Grain boundaries","Materials informatics","Molecular dynamics","Pair correlation function","Principal component analysis","Process-structure-property linkage","Interatomic potentials","Multiscale modeling"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1853/60125","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Kalidindi, Surya R."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["McDowell, David L.","Li, Mo","Haaland, Benjamin","Garmestani, Hamid"]},{"key":"dc:contributor.department","label":"Department","values":["Materials Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Gomberg, Joshua A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-08-20T15:28:03Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-08-20T15:28:03Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-05-23"]},{"key":"dc:publisher","label":"Institution","values":["Georgia Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Doctoral"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Grain boundaries","Materials informatics","Molecular dynamics","Pair correlation function","Principal component analysis","Process-structure-property linkage","Interatomic potentials","Multiscale modeling"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1853/60125"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["For a variety of materials, atomic-scale modeling techniques are commonly employed as a means of investigating fundamental properties, including both structural and chemical responses. While force-field based calculations are significantly less computationally expensive than their quantum-mechanical counterparts, the datasets often investigated are large in size (10^3 – 10^9 atoms) and high-dimensional, and thus cumbersome for use in multi-scale models. The development of quantitative “process-structure-property” (PSP) linkages for atomistic simulations presents a powerful route to convert atomistic simulation data into actionable knowledge. Here, a framework is presented for quantifying structure from these simulations in full- and reduced-dimensional form, and a series of protocols are developed for establishing regression models for process-structure and structure-property linkages."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Data-driven PSP linkages for atomistic datasets"]}]}],"canonical_facts":{"dc:contributor.advisor":["Kalidindi, Surya R."],"dc:contributor.committeemember":["McDowell, David L.","Li, Mo","Haaland, Benjamin","Garmestani, Hamid"],"dc:contributor.department":["Materials Science and Engineering"],"dc:creator":["Gomberg, Joshua A."],"dc:date.accessioned":["2018-08-20T15:28:03Z"],"dc:date.available":["2018-08-20T15:28:03Z"],"dc:date.issued":["2017-05-23"],"dc:description.abstract":["For a variety of materials, atomic-scale modeling techniques are commonly employed as a means of investigating fundamental properties, including both structural and chemical responses. While force-field based calculations are significantly less computationally expensive than their quantum-mechanical counterparts, the datasets often investigated are large in size (10^3 – 10^9 atoms) and high-dimensional, and thus cumbersome for use in multi-scale models. The development of quantitative “process-structure-property” (PSP) linkages for atomistic simulations presents a powerful route to convert atomistic simulation data into actionable knowledge. Here, a framework is presented for quantifying structure from these simulations in full- and reduced-dimensional form, and a series of protocols are developed for establishing regression models for process-structure and structure-property linkages."],"dc:description.degree":["Ph.D."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1853/60125"],"dc:language.iso":["en_US"],"dc:publisher":["Georgia Institute of Technology"],"dc:subject":["Grain boundaries","Materials informatics","Molecular dynamics","Pair correlation function","Principal component analysis","Process-structure-property linkage","Interatomic potentials","Multiscale modeling"],"dc:title":["Data-driven PSP linkages for atomistic datasets"],"dc:type":["Text"],"thesis:degree_level":["Doctoral"]},"updated_at":"2026-07-27T19:50:24Z"}