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Optimizing transition states via kernel-based machine learning.
| Content Provider | Semantic Scholar |
|---|---|
| Author | Pozun, Zachary D. Hansen, Katja Sheppard, Daniel Crispin Rupp, Matthias Mueller, K. Henkelman, Graeme A. |
| Copyright Year | 2012 |
| 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. |
| Starting Page | 174101 |
| Ending Page | 174101 |
| Page Count | 1 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://cacs.usc.edu/education/cs653/Pozun-TransitionStateLearning-JCP12.pdf |
| Alternate Webpage(s) | http://theory.cm.utexas.edu/henkelman/pubs/pozun12_174101.pdf |
| PubMed reference number | 22583204v1 |
| Alternate Webpage(s) | https://doi.org/10.1063/1.4707167 |
| DOI | 10.1063/1.4707167 |
| Journal | The Journal of chemical physics |
| Volume Number | 136 |
| Issue Number | 17 |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Extraction Intuition Kernel Molecular Dynamics Support Vector Machine |
| Content Type | Text |
| Resource Type | Article |
