CCL: an algorithm for the efficient comparison of clusters

Abstract

The systematic comparison of the atomic structure of solids and clusters has become an important task in crystallography, chemistry, physics and materials science, in particular in the context of structure prediction and structure determination of nanomaterials. In this work, an efficient and robust algorithm for the comparison of cluster structures is presented, which is based on the mapping of the point patterns of the two clusters onto each other. This algorithm has been implemented as the module CCL in the structure visualization and analysis program KPLOT.

Chiral effects on helicity studied via the energy landscape of short (d, l)-alanine peptides

“…As we discovered a number of structures in which one or more alanine residues were partially involved in the formation of a helix, we used the cluster compare algorithm 35 to analyse such structures. Here, we check for the existence of a subunit of a perfect right-or left-handed alpha helix comprising n (n = 1-6) alanine residues in all the structures found.…”

Prediction of possible CaMnO3modifications using anab initiominimization data-mining approach

“…Furthermore, our recent experimental results (Bošković et al, 2008; show that properties of CaMnO 3 nanopowders can be affected by doping. In that respect, the newly developed CCL algorithm implemented in the KPLOT program (Hundt et al, 2013) could help investigate nanosized structures.…”

An efficient genetic algorithm for structure prediction at the nanoscale

“…There are a number of algorithms in the literature that have been designed to recognise similar structures and/or measure the degree of similarity. [37][38][39] Although these solutions are comprehensive, the associated computational costs are also high, while information which is not necessary for the current objective is also generated by these procedures.…”

“…New developments in the GA module address the well-known issue of creating and maintaining structural diversity within the GA population of nanoclusters. The power of this approach is demonstrated by searching for atomic structures of LJ 38 ; an example of a challenging double funnel energy landscape.…”