Computing anharmonic infrared spectra of polycyclic aromatic hydrocarbons using machine learning molecular dynamics

Abstract

We introduce a machine learning molecular dynamics (MLMD) approach to calculate the anharmonic infrared (IR) absorption spectra of polycyclic aromatic hydrocarbons (PAHs), key carriers of interstellar aromatic IR bands. This method accounts for temperature effects in a molecule-specific way and achieves accuracy comparable to conventional quantum chemical calculations at a fraction of the cost, scaling linearly with system size. We applied MLMD to calculate the anharmonic spectra of 1704 PAHs in the NASA Ames PAH IR Spectroscopic Data base with up to 216 carbon atoms at different temperatures, demonstrating its capability for high-throughput spectral calculations of large molecular systems. Our results highlight MLMD’s potential to enable the development of extensive molecular spectral data sets, enhancing data-driven analyses of astronomical IR spectra, particularly in anticipation of upcoming observations from the James Webb Space Telescope.