Polycyclic aromatic hydrocarbons (PAHs) are among the most abundant molecules in the interstellar medium. Their characteristic infrared (IR) emission acts as a sensitive probe of astrophysical environments, yet detailed spectral analyses have been limited by the high computational cost of density functional theory (DFT) calculations. This constraint has hindered a systematic exploration of how spectral features such as the aromatic IR bands depend on a PAH's charge state and molecular structure. Our goal is to develop a computationally efficient machine learning model capable of predicting IR spectra for PAHs across charge states, and to critically reassess established interpretations of how ionization influences these spectra. We developed a neural network framework to predict PAH IR spectra across four charge states, utilizing a dataset of 12599 species. Molecular structures were represented by topological fingerprints, with charge states integrated via learnable embeddings. Additionally, a random forest classifier was implemented to infer charge states directly from spectral data. The model achieves near-DFT accuracy in predicting IR spectra while offering orders-of-magnitude acceleration in computation. It reliably handles PAHs containing up to 150 carbon atoms, including anions, neutrals, cations, and di-cations. The predictive capability for larger molecules is currently limited by the available training data. The classifier predicts charge states with over 99% accuracy. Our analysis of the DFT-computed spectra shows that anions exhibit strong emission across multiple bands, often matching or exceeding cation intensities, and the 11.2 micrometer band shows a distinct charge dependence.
He et al. (Wed,) studied this question.
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