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    Data-driven PSP linkages for atomistic datasets

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    GOMBERG-DISSERTATION-2017.pdf (4.348Mb)
    Date
    2017-05-23
    Author
    Gomberg, Joshua A.
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    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.
    URI
    http://hdl.handle.net/1853/60125
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    • Georgia Tech Theses and Dissertations [23877]
    • School of Materials Science and Engineering Theses and Dissertations [986]

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