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Deep learning for characterizing the self-assembly of three-dimensional colloidal systems.
O'Leary, Jared; Mao, Runfang; Pretti, Evan J; Paulson, Joel A; Mittal, Jeetain; Mesbah, Ali.
Afiliação
  • O'Leary J; Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA. mesbah@berkeley.edu.
Soft Matter ; 17(4): 989-999, 2021 Jan 28.
Article em En | MEDLINE | ID: mdl-33284930
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.

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Soft Matter Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Idioma: En Revista: Soft Matter Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos