2010/12/29 by Peter Ronhovde, Saurish Chakrabarty, Ronhovde, P. +11 · 1 citation
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Computational Physics (physics.comp-ph) #Data Analysis #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #Soft Condensed Matter (cond-mat.soft) #Statistics and Probability (physics.data-an) #Theoretical and Computational Physics #Topological and Geometric Data Analysis
paper · pdf · doi:10.48550/arxiv.1101.0008
openalex publication_date 2010/12/29 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
Recent decades have seen the discovery of numerous complex materials. At the root of the complexity underlying many of these materials lies a large number of possible contending atomic- and larger-scale configurations and the intricate correlations between their constituents. For a detailed understanding, there is a need for tools that enable the detection of pertinent structures on all spatial and temporal scales. Towards this end, we suggest a new method by invoking ideas from network analysis and information theory. Our method efficiently identifies basic unit cells and topological defects in systems with low disorder and may analyze general amorphous structures to identify candidate natural structures where a clear definition of order is lacking. This general unbiased detection of physical structure does not require a guess as to which of the system properties should be deemed as important and may constitute a natural point of departure for further analysis. The method applies to both static and dynamic systems.