Characterizing complex particle morphologies through shape matching: Descriptors, applications, and algorithms

Many standard structural quantities, such as order parameters and correlation functions, exist for common condensed matter systems, such as spherical and rod-like particles. However, these structural quantities are often insufficient for characterizing the unique and highly complex structures often...

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Vydané v:Journal of computational physics Ročník 230; číslo 17; s. 6438 - 6463
Hlavní autori: Keys, Aaron S., Iacovella, Christopher R., Glotzer, Sharon C.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Kidlington Elsevier Inc 20.07.2011
Elsevier
Predmet:
Algorithms
Computational techniques
Computer simulation
Correlation
Exact sciences and technology
Matching
Mathematical methods in physics
Mathematical models
Nanostructure
Order parameters
Physics
Self assembly
Self-assembly
Order parameters
Phase diagrams
Spatial correlation
Correlation functions
Calculation methods
Algorithms
Morphology
Particle system
Classification
Positions
Calculation
Particle shape
ISSN:0021-9991, 1090-2716
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Popis
Shrnutí:Many standard structural quantities, such as order parameters and correlation functions, exist for common condensed matter systems, such as spherical and rod-like particles. However, these structural quantities are often insufficient for characterizing the unique and highly complex structures often encountered in the emerging field of nano and microscale self-assembly, or other disciplines involving complex structures such as computational biology. Computer science algorithms known as “shape matching” methods pose a unique solution to this problem by providing robust metrics for quantifying the similarity between pairs of arbitrarily complex structures. This pairwise matching operation, either implicitly or explicitly, lies at the heart of most standard structural characterization schemes for particle systems. By substituting more robust “shape descriptors” into these schemes we extend their applicability to structures formed from more complex building blocks. Here, we describe several structural characterization schemes and shape descriptors that can be used to obtain various types of structural information about particle systems. We demonstrate the application of shape matching algorithms to a variety of example problems, for topics including local and global structure identification and classification, automated phase diagram mapping, and the construction of spatial and temporal correlation functions. The methods are applicable to a wide range of systems, both simulated and experimental, provided particle positions are known or can be accurately imaged.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2011.04.017