Toward Accurate PAH IR Spectra Prediction: Handling Charge Effects with Classical and Deep Learning Models

Polycyclic aromatic hydrocarbons (PAHs) play a crucial role in astrochemistry, environmental studies, and combustion chemistry, yet interpreting their infrared (IR) spectra remains challenging due to the similarity of spectral features of many molecules. The presumable presence of both neutral and charged PAHs in mixtures complicates spectra interpretation, too. While first-principle calculations provide accurate spectral predictions, their high computational cost limits scalability. This study employs machine learning (ML) to predict PAH IR spectra, emphasizing the applicability of the developed models simultaneously for neutral and ionized molecules. Two models are introduced: an XGBoost model trained on Morgan fingerprints and a graph neural network (GNN) that employs molecular graph representations. Molecular charges are treated by incorporating their one-hot or learnable NN encodings to molecular representations. Both models demonstrate excellent predictive capabilities, for the first time enabling fast and accurate prediction of charged PAHs IR spectra. While the XGBoost model demonstrates the highest accuracy achieved to date, the GNN shows significant promise for future advancements due to the inherent capabilities of molecular graph representations. Remaining challenges, such as the scarcity of data on heteroatomic PAHs, and potential approaches of addressing them are also discussed in the manuscript.