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

Monte Carlo;Scientific Computing;Self-assembly;BUBBA;Chemical Engineering;Engineering;Chemical Engineering

Jankowski, EricZiff, Robert M. ;

University of Michigan

关键词: Monte Carlo;    Scientific Computing;    Self-assembly;    BUBBA;    Chemical Engineering;    Engineering;    Chemical Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/91509/erjank_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

We 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.