Machine learning identified molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons
Zhisen Meng, Yong Zhang, En‐Wei Liang, Zhao Wang
Abstract
ABSTRACT 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 $1172.745\, \mu$m. 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 superhydrogenated PAHs.