  Using machine learning, we explore the utility of various deep neural
networks (NN) when applied to high harmonic generation (HHG) scenarios. First,
we train the NNs to predict the time-dependent dipole and spectra of HHG
emission from reduced-dimensionality models of di- and triatomic systems based
of on sets of randomly generated parameters (laser pulse intensity,
internuclear distance, and molecular orientation). These networks, once
trained, are useful tools to rapidly generate the HHG spectra of our systems.
Similarly, we have trained the NNs to solve the inverse problem - to determine
the molecular parameters based on HHG spectra or dipole acceleration data.
These types of networks could then be used as spectroscopic tools to invert HHG
spectra in order to recover the underlying physical parameters of a system.
Next, we demonstrate that transfer learning can be applied to our networks to
expand the range of applicability of the networks with only a small number of
new test cases added to our training sets. Finally, we demonstrate NNs that can
be used to classify molecules by type: di- or triatomic, symmetric or
asymmetric, wherein we can even rely on fairly simple fully connected neural
networks. With outlooks toward training with experimental data, these NN
topologies offer a novel set of spectroscopic tools that could be incorporated
into HHG experiments.
