.png){kind=icon}
.png){kind=icon}
Return
Optimizing transition states via kernel-based machine learning
.png){kind=icon}
PRE
AI
DOI：10.1063/1.4707167.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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]
Keywords:
MOLECULAR-DYNAMICS
SURFACE
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.1
Papers:
7.2W
Citations:
23.2W
