Accelerating materials discovery and design: computational study of the structure and properties of materials

dc.contributor.advisor Kai-Ming Ho Zhao, Xin
dc.contributor.department Physics and Astronomy 2018-08-11T15:01:17.000 2020-06-30T02:56:48Z 2020-06-30T02:56:48Z Thu Jan 01 00:00:00 UTC 2015 2001-01-01 2015-01-01
dc.description.abstract <p>This thesis summarizes our efforts to study the structure and properties of materials computationally. The adaptive genetic algorithm (AGA) developed by us to predict crystal/surface/interface structures is presented. Applications of AGA to a variety of systems, such as non-rare earth magnetic materials, ultra-hard transition metal borides and SrTiO3 grain boundaries, are discussed. We demonstrated by AGA the capability of solving crystal structures with more than 100 atoms per unit cell and rapidly accessing the structures and phase stabilities of different compositions in multicomponent systems. We also introduced a motif-network scheme to study the complex crystal structures in silicate cathodes. In addition, we explored different computational methods for atomistic simulations of materials behavior, such as Monte Carlo modeling of the alnico magnets.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 5455
dc.identifier.contextkey 7936121
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath etd/14448
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 20:20:23 UTC 2022
dc.subject.disciplines Condensed Matter Physics
dc.subject.disciplines Physics
dc.subject.keywords Condensed Matter Physics
dc.subject.keywords Computational material discovery and design
dc.subject.keywords Crystal structure prediction
dc.subject.keywords Density Functional Theory
dc.subject.keywords Genetic Algorithm
dc.title Accelerating materials discovery and design: computational study of the structure and properties of materials
dc.type article
dc.type.genre dissertation
dspace.entity.type Publication
relation.isOrgUnitOfPublication 4a05cd4d-8749-4cff-96b1-32eca381d930 dissertation Doctor of Philosophy
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