Optimizing transition states via kernel-based machine learning
- Authors
- Pozun, Zachary D.; Hansen, Katja; Sheppard, Daniel; Rupp, Matthias; Mueller, Klaus-Robert; Henkelman, Graeme
- Issue Date
- 7-5월-2012
- Publisher
- AMER INST PHYSICS
- Keywords
- Distance-based; Dividing surfaces; Low energies; Machine-learning; Priori information; Reaction mechanism; Saddle point; Transition state; Transition state theories; Transmission coefficients; Learning systems; Molecular dynamics; Reaction kinetics; Optimization
- Citation
- JOURNAL OF CHEMICAL PHYSICS, v.136, no.17
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF CHEMICAL PHYSICS
- Volume
- 136
- Number
- 17
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/108441
- DOI
- 10.1063/1.4707167
- ISSN
- 0021-9606
- 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. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4707167]
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.