English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  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.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Pozun, Zachary D.1, 2, Author
Hansen, Katja1, 3, Author           
Sheppard, Daniel1, 2, Author
Rupp, Matthias1, 3, Author
Müller, Klaus-Robert1, 3, 4, Author
Henkelman, Graeme1, 2, Author
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              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2012-01-252012-04-112012-05-012012-05-07
 Publication Status: Issued
 Pages: 8
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.4707167
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: The Journal of Chemical Physics
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 136 (17) Sequence Number: 174101 Start / End Page: - Identifier: ISSN: 1520-9032
CoNE: https://pure.mpg.de/cone/journals/resource/991042752807952