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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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Ionic Crystal Structures02:42

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Crystallization is a phase transformation process in which crystals are precipitated from a supersaturated solution or formed from other sources. During crystallization, atoms or molecules arrange themselves into a well-defined, rigid crystal lattice to minimize energy.
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Related Experiment Video

Updated: Feb 8, 2026

High-Contrast and Fast Photorheological Switching of a Twist-Bend Nematic Liquid Crystal
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Automated crystal characterization with a fast neighborhood graph analysis method.

Wesley F Reinhart1, Athanassios Z Panagiotopoulos

  • 1Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA. azp@princeton.edu.

Soft Matter
|July 11, 2018
PubMed
Summary

We developed a faster Neighborhood Graph Analysis method for crystal structure characterization. This technique efficiently analyzes local neighborhoods, significantly reducing computational costs for classifying complex crystal structures.

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Area of Science:

  • Materials Science
  • Crystallography
  • Computational Science

Background:

  • Template-free characterization of crystal structures is crucial for materials discovery.
  • Existing methods for crystal structure analysis can be computationally intensive and limited in scope.
  • Neighborhood Graph Analysis (NGA) offers a promising approach but requires optimization.

Purpose of the Study:

  • To present a significantly improved and highly efficient implementation of the Neighborhood Graph Analysis (NGA) technique.
  • To enable template-free characterization of crystal structures directly from particle tracking data.
  • To establish protocols for automated detection of topological features and color assignment.

Main Methods:

  • Implemented a computationally efficient NGA by comparing local neighborhoods based on graphlet frequencies.
  • Reduced computational cost by four orders of magnitude compared to the original stochastic NGA method.
  • Developed automated protocols for identifying key topological structures and assigning informative colors.

Main Results:

  • Achieved a substantial reduction in computational cost for crystal structure characterization.
  • Demonstrated a fully automated procedure for analyzing particle tracking data.
  • Successfully applied the improved NGA to a diverse range of challenging crystal structures.

Conclusions:

  • The enhanced NGA provides a rapid and robust method for crystal structure characterization.
  • This technique offers a flexible and automated solution for analyzing complex crystalline materials.
  • The improved NGA significantly advances the field of template-free crystal structure analysis.