Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 136, Issue 17, Pages -Publisher
AIP Publishing
DOI: 10.1063/1.4707167
Keywords
-
Funding
- Institute of Pure and Applied Mathematics at UCLA
- National Science Foundation [CHE-0645497]
- Dorothy B. Banks Fellowship
- European Community [PASCAL2]
- Deutsche Forschungsgemeinschaft (DFG) [MU 987/4-2]
- World Class University through the National Research Foundation of Korea
- Ministry of Education, Science, and Technology [R31-10008]
Ask authors/readers for more resources
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]
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available