Deep learning for characterizing the self-assembly of three-dimensional colloidal systems. Academic Article uri icon

abstract

  • Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems' stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. We first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly.

published proceedings

  • Soft Matter

altmetric score

  • 6.75

author list (cited authors)

  • O'Leary, J., Mao, R., Pretti, E. J., Paulson, J. A., Mittal, J., & Mesbah, A.

citation count

  • 5

complete list of authors

  • O‚ÄôLeary, Jared||Mao, Runfang||Pretti, Evan J||Paulson, Joel A||Mittal, Jeetain||Mesbah, Ali

publication date

  • January 2021