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 journalArticle

51 Citations (Scopus)

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 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
Volume136
Issue number17
DOIs
Publication statusPublished - 2012 May 7

ASJC Scopus subject areas

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

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