Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 139, Issue 18, Pages -Publisher
AIP Publishing
DOI: 10.1063/1.4828704
Keywords
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Funding
- Swiss National Science Foundation (SNF)
- Swiss National Center of Competence in Research (NCCR) on Nanoscale Science
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In order to characterize molecular structures we introduce configurational fingerprint vectors which are counterparts of quantities used experimentally to identify structures. The Euclidean distance between the configurational fingerprint vectors satisfies the properties of a metric and can therefore safely be used to measure dissimilarities between configurations in the high dimensional configuration space. In particular we show that these metrics are a perfect and computationally cheap replacement for the root-mean-square distance (RMSD) when one has to decide whether two noise contaminated configurations are identical or not. We introduce a Monte Carlo approach to obtain the global minimum of the RMSD between configurations, which is obtained from a global minimization over all translations, rotations, and permutations of atomic indices. (C) 2013 AIP Publishing LLC.
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