  Despite their importance in a wide variety of applications, the estimation of
ionization cross sections for large molecules continues to present challenges
for both experiment and theory. Machine learning algorithms have been shown to
be an effective mechanism for estimating cross section data for atomic targets
and a select number of molecular targets. We present an efficient machine
learning model for predicting ionization cross sections for a broad array of
molecular targets. Our model is a 3-layer neural network that is trained using
published experimental datasets. There is minimal input to the network, making
it widely applicable. We show that with training on as few as 10 molecular
datasets, the network is able to predict the experimental cross sections of
additional molecules with an accuracy similar to experimental uncertainties in
existing data. As the number of training molecular datasets increased, the
network's predictions became more accurate and, in the worst case, were within
30% of accepted experimental values. In many cases, predictions were within 10%
of accepted values. Using a network trained on datasets for 25 different
molecules, we present predictions for an additional 27 molecules, including
alkanes, alkenes, molecules with ring structures, and DNA nucleotide bases.
