Title: Constructing grids for molecular quantum dynamics using an autoencoder,
Abstract: A challenge for molecular quantum dynamics (QD) calculations is the curse of
dimensionality with respect to the nuclear degrees of freedom. A common
approach that works especially well for fast reactive processes is to reduce
the dimensionality of the system to a few most relevant coordinates.
Identifying these can become a very difficult task, since they often are highly
unintuitive. We present a machine learning approach that utilizes an
autoencoder that is trained to find a low-dimensional representation of a set
of molecular configurations. These configurations are generated by trajectory
calculations performed on the reactive molecular systems of interest. The
resulting low-dimensional representation can be used to generate a potential
energy surface grid in the desired subspace. Using the G-matrix formalism to
calculate the kinetic energy operator, QD calculations can be carried out on
this grid. In addition to step-by-step instructions for the grid construction,
we present the application to a test system.