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dc.contributor.advisorKalidindi, Surya R.
dc.contributor.authorGomberg, Joshua A.
dc.date.accessioned2018-08-20T15:28:03Z
dc.date.available2018-08-20T15:28:03Z
dc.date.created2017-08
dc.date.issued2017-05-23
dc.date.submittedAugust 2017
dc.identifier.urihttp://hdl.handle.net/1853/60125
dc.description.abstractFor 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.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectGrain boundaries
dc.subjectMaterials informatics
dc.subjectMolecular dynamics
dc.subjectPair correlation function
dc.subjectPrincipal component analysis
dc.subjectProcess-structure-property linkage
dc.subjectInteratomic potentials
dc.subjectMultiscale modeling
dc.titleData-driven PSP linkages for atomistic datasets
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentMaterials Science and Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberMcDowell, David L.
dc.contributor.committeeMemberLi, Mo
dc.contributor.committeeMemberHaaland, Benjamin
dc.contributor.committeeMemberGarmestani, Hamid
dc.date.updated2018-08-20T15:28:03Z


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