  The in silico exploration of chemical, physical and biological systems
requires accurate and efficient energy functions to follow their nuclear
dynamics at a molecular and atomistic level. Recently, machine learning tools
gained a lot of attention in the field of molecular sciences and simulations
and are increasingly used to investigate the dynamics of such systems. Among
the various approaches, artificial neural networks (NNs) are one promising tool
to learn a representation of potential energy surfaces. This is done by
formulating the problem as a mapping from a set of atomic positions
$\mathbf{x}$ and nuclear charges $Z_i$ to a potential energy $V(\mathbf{x})$.
Here, a fully-dimensional, reactive neural network representation for
malonaldehyde (MA), acetoacetaldehyde (AAA) and acetylacetone (AcAc) is
learned. It is used to run finite-temperature molecular dynamics simulations,
and to determine the infrared spectra and the hydrogen transfer rates for the
three molecules. The finite-temperature infrared spectrum for MA based on the
NN learned on MP2 reference data provides a realistic representation of the
low-frequency modes and the H-transfer band whereas the CH vibrations are
somewhat too high in frequency. For AAA it is demonstrated that the IR
spectroscopy is sensitive to the position of the transferring hydrogen at
either the OCH- or OCCH$_3$ end of the molecule. For the hydrogen transfer
rates it is demonstrated that the O-O vibration is a gating mode and largely
determines the rate at which the hydrogen is transferred between the donor and
acceptor. Finally, possibilities to further improve such NN-based potential
energy surfaces are explored. They include the transferability of an NN-learned
energy function across chemical species (here methylation) and transfer
learning from a lower level of reference data (MP2) to a higher level of theory
(pair natural orbital-LCCSD(T)).
