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Optimizing transition states via kernel-based machine learning

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Hansen,  Katja
Institute for Pure and Applied Mathematics, University of California, Los Angeles,;
Theory, Fritz Haber Institute, Max Planck Society;

Rupp,  Matthias
Institute for Pure and Applied Mathematics, University of California, Los Angeles,;
Theory, Fritz Haber Institute, Max Planck Society;

Müller,  Klaus-Robert
Institute for Pure and Applied Mathematics, University of California, Los Angeles,;
Theory, Fritz Haber Institute, Max Planck Society;
Department of Brain and Cognitive Engineering, Korea University;

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Citation

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


Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-76E1-6
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 machinelearned 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.