Machine-learning identified molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons

TL;DR

This study employs machine learning to identify specific molecular fragments in polycyclic aromatic hydrocarbons responsible for their infrared emission features, aiding in understanding unidentified infrared bands.

Contribution

It introduces a machine learning approach using random forests and molecular fingerprints to pinpoint structural features linked to IR spectra of PAHs, providing a new reference for UIE carrier analysis.

Findings

Identified key molecular fragments associated with IR emission features.

Validated results against known unidentified infrared emission bands.

Provided comprehensive tables for future spectral and structural analysis.

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 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.