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キーワード:
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要旨:
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.
