Comparing distance metrics for rotation using the k-nearest neighbors algorithm for entropy estimation
Copyright © 2013 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 35(2014), 5 vom: 15. Feb., Seite 377-85 |
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| 1. Verfasser: | |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2014
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Comparative Study Journal Article Research Support, Non-U.S. Gov't distance metric entropy k-nearest neighbors molecular dynamics solvation statistical mechanics Water |
| Zusammenfassung: | Copyright © 2013 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc. Distance metrics facilitate a number of methods for statistical analysis. For statistical mechanical applications, it is useful to be able to compute the distance between two different orientations of a molecule. However, a number of distance metrics for rotation have been employed, and in this study, we consider different distance metrics and their utility in entropy estimation using the k-nearest neighbors (KNN) algorithm. This approach shows a number of advantages over entropy estimation using a histogram method, and the different approaches are assessed using uniform randomly generated data, biased randomly generated data, and data from a molecular dynamics (MD) simulation of bulk water. The results identify quaternion metrics as superior to a metric based on the Euler angles. However, it is demonstrated that samples from MD simulation must be independent for effective use of the KNN algorithm and this finding impacts any application to time series data |
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| Beschreibung: | Date Completed 05.09.2014 Date Revised 21.03.2024 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.23504 |