  The Interest in polycyclic aromatic hydrocarbons (PAHs) spans numerous fields
and infrared spectroscopy is usually the method of choice to disentangle their
molecular structure. In order to compute vibrational frequencies, numerous
theoretical studies employ either quantum calculation methods, or empirical
potentials, but it remains difficult to combine the accuracy of the first
approach with the computational cost of the second. In this work, we employed
Machine Learning techniques to develop a potential energy surface and a dipole
mapping based on an artificial neural network (ANN) architecture. Altogether,
while trained on only 11 small PAH molecules, the obtained ANNs are able to
retrieve the infrared spectra of those small molecules, but more importantly of
8 large PAHs different from the training set, thus demonstrating the
transferability of our approach.
