Using machine learning, we explore the utility of various deep neural networks (NN) when applied to high harmonic generation (HHG) scenarios. First, train NNs predict time-dependent dipole and spectra HHG emission from reduced-dimensionality models di- triatomic systems based on sets randomly generated parameters (laser pulse intensity, internuclear distance, molecular orientation). These netwo...