title: Optimizing transition states via kernel-based machine learning.
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.
authors.affiliation: Institute for Pure and Applied Mathematics, University of California, Los Angeles, Los Angeles, California 90095-7121, USA.
authors.forename: Zachary D
authors.initials: ZD
authors.lastname: Pozun
authors.forename: Katja
authors.initials: K
authors.lastname: Hansen
authors.forename: Daniel
authors.initials: D
authors.lastname: Sheppard
authors.forename: Matthias
authors.initials: M
authors.lastname: Rupp
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Graeme
authors.initials: G
authors.lastname: Henkelman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D056004:Molecular Dynamics Simulation; D012984:Software; D060388:Support Vector Machine; D013499:Surface Properties