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Metrics for measuring distances in configuration spaces.
- Source :
-
The Journal of chemical physics [J Chem Phys] 2013 Nov 14; Vol. 139 (18), pp. 184118. - Publication Year :
- 2013
-
Abstract
- 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.
- Subjects :
- Monte Carlo Method
Quantum Theory
Subjects
Details
- Language :
- English
- ISSN :
- 1089-7690
- Volume :
- 139
- Issue :
- 18
- Database :
- MEDLINE
- Journal :
- The Journal of chemical physics
- Publication Type :
- Academic Journal
- Accession number :
- 24320265
- Full Text :
- https://doi.org/10.1063/1.4828704