  Machine learning feature importance calculations are used to determine the
molecular substructures that are responsible for mid and far-infrared (IR)
emission features of neutral polycyclic aromatic hydrocarbons (PAHs). Using the
extended-connectivity fingerprint as a descriptor of chemical structure, a
random forest model is trained on the spectra of 14,124 PAHs to evaluate the
importance of 10,632 molecular fragments for each band within the range of
2.761 to 1172.745 microns. The accuracy of the results is confirmed by
comparing them with previously studied unidentified infrared emission (UIE)
bands. The results are summarized in two tables available as Supplementary
Data, which can be used as a reference for assessing possible UIE carriers. We
demonstrate that the tables can be used to explore the relation between the PAH
structure and the spectra by discussing about the IR features of
nitrogen-containing PAHs and super-hydrogenated PAHs.
