2023MNRAS.524..361Z

The identification of molecules in exoplanetary atmospheres is only possible thanks to the availability of high-resolution molecular spectroscopic data. However, due to its intensive and time-consuming generation process, at present, only 100 molecules have high-resolution spectroscopic data available, limiting new molecular detections. Using routine quantum chemistry calculations (i.e. scaled harmonic frequency calculations using the B97-1/def2-TZVPD model chemistry with median errors of 10 cm^–1), here we present a complementary high-throughput approach to rapidly generate approximate vibrational spectral data for 2743 molecules made from the biologically most important elements C, H, N, O, P, and S. Though these data are not accurate enough to enable definitive molecular detections and do not seek to replace the need for high-resolution data, it has powerful applications in identifying potential molecular candidates responsible for unknown spectral features. We explore this application for the $4.1\,\mu{\rm m}$ (2439 cm^–1) feature in the atmospheric spectrum of WASP-39b, listing potential alternative molecular species responsible for this spectral line, together with SO₂. Further applications of this big data compilation also include identifying molecules with strong absorption features that are likely detectable at quite low abundances and providing a training set for machine learning predictions of vibrational frequencies. Characterizing exoplanetary atmospheres through molecular spectroscopy is essential to understanding the planet's physico-chemical processes and likelihood of hosting life. Our rapidly generated quantum chemistry big data set will play a crucial role in supporting this understanding by giving directions into possible initial identifications of the more unusual molecules to emerge.