Reaction rates of chemical reactions under nonequilibrium conditions can be determined through the construction of the normally hyperbolic invariant manifold (NHIM) [and moving dividing surface (DS)] associated with the transition state trajectory. Here, we extend our recent methods by constructing points on the NHIM accurately even for multidimensional cases. We also advance the implementation of machine learning approaches to construct smooth versions of the NHIM from a known high-accuracy set of its points. That is, we expand on our earlier use of neural nets, and introduce the use of Gaussian process regression for the determination of the NHIM. Finally, we compare and contrast all of these methods for a challenging two-dimensional model barrier case so as to illustrate their accuracy and general applicability.
@article{arxiv.1903.06966,
title = {Invariant Manifolds and Rate Constants in Driven Chemical Reactions},
author = {Matthias Feldmaier and Philippe Schraft and Robin Bardakcioglu and Johannes Reiff and Melissa Lober and Martin Tschöpe and Andrej Junginger and Jörg Main and Thomas Bartsch and Rigoberto Hernandez},
journal= {arXiv preprint arXiv:1903.06966},
year = {2019}
}