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The Interest in polycyclic aromatic hydrocarbons (PAHs) spans numerous fieldsand infrared spectroscopy is usually the method of choice to disentangle theirmolecular structure. In order to compute vibrational frequencies, numeroustheoretical studies employ either quantum calculation methods, or empiricalpotentials, but it remains difficult to combine the accuracy of the firstapproach with the computational cost of the second. In this work, we employedMachine Learning techniques to develop a potential energy surface and a dipolemapping based on an artificial neural network (ANN) architecture. Altogether,while trained on only 11 small PAH molecules, the obtained ANNs are able toretrieve the infrared spectra of those small molecules, but more importantly of8 large PAHs different from the training set, thus demonstrating thetransferability of our approach.

Infrared spectra of neutral polycyclic aromatic hydrocarbons by machine learning