Optimizing transition states via kernel-based machine learning

Zachary D. Pozun, Katja Hansen, Daniel Sheppard, Matthias Rupp, Klaus Robert Müller, Graeme Henkelman

Research output: Contribution to journalArticlepeer-review

83 Citations (Scopus)


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 machine-learned 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.

Original languageEnglish
Article number174101
JournalJournal of Chemical Physics
Issue number17
Publication statusPublished - 2012 May 7

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry


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