Ethereal AI: Infrared Spectra of Polycyclic Aromatic Hydrocarbons with Machine Learning DFT Scaling Factors

Polycyclic aromatic hydrocarbons (PAHs) are of interest in astrochemistry and are studied through infrared (IR) signals from space. Deconvoluting the observed IR of PAHs often relies on computational chemistry for references. Current practice typically uses IR predicted by density functional theory adjusted with scaling factors to account for anharmonicity and basis set limitations. We present a machine learning approach to scaling factors that adjusts each predicted IR frequency individually based on computed frequencies, intensities, reduced masses, and force constants. Our machine learning model is able to obtain a mean absolute error (MAE) and maximum error of 5 and 13 cm–1, respectively, a drastic improvement over a current-practice scaling factor approach with an MAE and maximum error of 10 and 23 cm–1, respectively. Using machine learning to determine scaling of DFT-generated IR spectra shows promise for enabling higher quality predictions on larger systems than those that have previously been possible.