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Towards Understanding the Self-assembly of Complicated Particles via Computation.

dc.contributor.authorJankowski, Ericen_US
dc.date.accessioned2012-06-15T17:30:49Z
dc.date.available2013-07-01T14:33:04Zen_US
dc.date.issued2012en_US
dc.date.submitteden_US
dc.identifier.urihttps://hdl.handle.net/2027.42/91509
dc.description.abstractWe develop advanced Monte Carlo sampling schemes and new methods of calculating thermodynamic partition functions that are used to study the self-assembly of complicated ``patchy '' particles. Patchy particles are characterized by their strong anisotropic interactions, which can cause critical slowing down in Monte Carlo simulations of their self-assembly. We prove that detailed balance is maintained for our implementation of Monte Carlo cluster moves that ameliorate critical slowing down and use these simulations to predict the structures self-assembled by patchy tetrominoes. We compare structures predicted from our simulations with those generated by an alternative learning-augmented Monte Carlo approach and show that the learning-augmented approach fails to sample thermodynamic ensembles. We prove one way to maintain detailed balance when parallelizing Monte Carlo using the checkerboard domain decomposition scheme by enumerating the state-to-state transitions for a simple model with general applicability. Our implementation of checkerboard Monte Carlo on graphics processing units enables accelerated sampling of thermodynamic properties and we use it to confirm the fluid-hexatic transition observed at high packing fractions of hard disks. We develop a new method, bottom-up building block assembly, which generates partition functions hierarchically. Bottom-up building block assembly provides a means to answer the question of which structures are favored at a given temperature and allows accelerated prediction of potential energy minimizing structures, which are difficult to determine with Monte Carlo methods. We show how the sequences of clusters generated by bottom-up building block assembly can be used to inform ``assembly pathway engineering'', the design of patchy particles whose assembly propensity is optimized for a target structure. The utility of bottom-up building block assembly is demonstrated for systems of CdTe/CdS tetrahedra, DNA-tethered nanospheres, colloidal analogues of patchy tetrominoes and shape-shifting particles.en_US
dc.language.isoen_USen_US
dc.subjectMonte Carloen_US
dc.subjectScientific Computingen_US
dc.subjectSelf-assemblyen_US
dc.subjectBUBBAen_US
dc.titleTowards Understanding the Self-assembly of Complicated Particles via Computation.en_US
dc.typeThesisen_US
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineChemical Engineeringen_US
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studiesen_US
dc.contributor.committeememberGlotzer, Sharon C.en_US
dc.contributor.committeememberHolland, John H.en_US
dc.contributor.committeememberLarson, Ronald G.en_US
dc.contributor.committeememberWoolf, Peter Jamesen_US
dc.contributor.committeememberZiff, Robert M.en_US
dc.subject.hlbsecondlevelChemical Engineeringen_US
dc.subject.hlbtoplevelEngineeringen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/91509/1/erjank_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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