Data-driven PSP linkages for atomistic datasets

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.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Materials Science and Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Gomberg, Joshua A.

Advisor dc:contributor.advisor

• Kalidindi, Surya R.

Committee members dc:contributor.committeemember

• McDowell, David L.
• Li, Mo
• Haaland, Benjamin
• Garmestani, Hamid

• Grain boundaries
• Materials informatics
• Molecular dynamics
• Pair correlation function
• Principal component analysis
• Process-structure-property linkage
• Interatomic potentials
• Multiscale modeling

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1853/60125

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/60125