English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Optimizing transition states via kernel-based machine learning

MPS-Authors
/persons/resource/persons45970

Hansen,  Katja
Institute for Pure and Applied Mathematics, University of California, Los Angeles,;
Theory, Fritz Haber Institute, Max Planck Society;

/persons/resource/persons173798

Rupp,  Matthias
Institute for Pure and Applied Mathematics, University of California, Los Angeles,;
Theory, Fritz Haber Institute, Max Planck Society;

Müller,  Klaus-Robert
Institute for Pure and Applied Mathematics, University of California, Los Angeles,;
Theory, Fritz Haber Institute, Max Planck Society;
Department of Brain and Cognitive Engineering, Korea University;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-76E1-6
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.