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  Optimizing transition states via kernel-based machine learning

Pozun, Z. D., Hansen, K., Sheppard, D., Rupp, M., Müller, K.-R., & Henkelman, G. (2012). Optimizing transition states via kernel-based machine learning. The Journal of Chemical Physics, 136(17): 174101. doi:10.1063/1.4707167.

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 Urheber:
Pozun, Zachary D.1, 2, Autor
Hansen, Katja1, 3, Autor           
Sheppard, Daniel1, 2, Autor
Rupp, Matthias1, 3, Autor           
Müller, Klaus-Robert1, 3, 4, Autor
Henkelman, Graeme1, 2, Autor
Affiliations:
1Institute for Pure and Applied Mathematics, University of California, Los Angeles,, Los Angeles, California 90095-7121, USA, ou_persistent22              
2Department of Chemistry and Biochemistry and the Institute for Computational Engineering and Sciences, The University of Texas at Austin,, Austin, Texas 78712-0165, USA, ou_persistent22              
3Theory, Fritz Haber Institute, Max Planck Society, Faradayweg 4-6, 14195 Berlin, ou_634547              
4Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Korea, ou_persistent22              

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 Zusammenfassung: We present a method for optimizing transition state theory dividing surfaces with support vector
machines. The resulting dividing surfaces require no a priori information or intuition about reaction
mechanisms. To generate optimal dividing surfaces, we apply a cycle of machine-learning
and refinement of the surface by molecular dynamics sampling. We demonstrate that the machinelearned
surfaces contain the relevant low-energy saddle points. The mechanisms of reactions may
be extracted from the machine-learned surfaces in order to identify unexpected chemically relevant
processes. Furthermore, we show that the machine-learned surfaces significantly increase the transmission
coefficient for an adatom exchange involving many coupled degrees of freedom on a (100)
surface when compared to a distance-based dividing surface.

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Sprache(n): eng - English
 Datum: 2012-01-252012-04-112012-05-012012-05-07
 Publikationsstatus: Erschienen
 Seiten: 8
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1063/1.4707167
 Art des Abschluß: -

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Titel: The Journal of Chemical Physics
Genre der Quelle: Zeitschrift
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 136 (17) Artikelnummer: 174101 Start- / Endseite: - Identifikator: ISSN: 1520-9032
CoNE: https://pure.mpg.de/cone/journals/resource/991042752807952