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Related Experiment Video

Updated: May 11, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

CCL: an algorithm for the efficient comparison of clusters.

R Hundt¹, J C Schön, S Neelamraju

¹Institut für Anorganische Chemie, Universität Bonn, Gerhard-Domagk-Strasse 1, D-53121 Bonn, Germany.

Journal of Applied Crystallography

|May 18, 2013

Summary

This summary is machine-generated.

This study introduces an efficient algorithm for comparing atomic structures in solids and clusters. The method aids in predicting and determining the structures of nanomaterials using the KPLOT program.

Keywords:

cluster structures computer programs nanomaterials structure determination structure prediction

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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Published on: February 15, 2017

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Published on: January 16, 2019

Area of Science:

Crystallography
Materials Science
Computational Chemistry
Nanomaterials Physics

Background:

Systematic comparison of atomic structures is crucial for solids and clusters.
This is particularly relevant for structure prediction and determination in nanomaterials research.

Purpose of the Study:

To present an efficient and robust algorithm for comparing cluster structures.
To implement this algorithm within the KPLOT software for practical application.

Main Methods:

Developed an algorithm based on mapping point patterns of two clusters.
Integrated the algorithm as the CCL module in the KPLOT program.

Main Results:

The presented algorithm offers an efficient and robust method for cluster structure comparison.
The CCL module in KPLOT facilitates practical application of this comparison technique.

Conclusions:

The developed algorithm and its implementation in KPLOT provide a valuable tool for nanomaterials research.
This work advances the field of atomic structure comparison for solids and clusters.