Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons

Abstract

Supervised machine learning models are trained with various molecular descriptors to predict infrared emission spectra of interstellar polycyclic aromatic hydrocarbons. We demonstrate that a feature importance analysis based on the random forest algorithm can be utilized to explore the physical correlation between emission features. Astronomical correlations between infrared bands are analyzed as examples of demonstration by finding the common molecular fragments responsible for different bands, which improves the current understanding of the long-observed correlations. We propose a way to quantify the band correlation by measuring the similarity of the feature importance arrays of different bands, via which a correlation map is obtained for emissions in the out-of-plane bending region. Moreover, a comparison between the predictions using different combinations of descriptors underscores the strong prediction power of the extended-connectivity molecular fingerprint, and shows that the combinations of multiple descriptors of other types in general lead to improved predictivity.

References in corpus (1)

• Variations of the Mid-IR Aromatic Features Inside and Among Galaxies

Cited by in corpus (4)

• Machine-learning identified molecular fragments responsible for infrared emission features of polycyclic aromatic hydrocarbons
• Evolution of Fullerenes in Circumstellar Envelopes by Carbon Condensation: Insights from Reactive Molecular Dynamics Simulations
• Catalytic role of HI in the interstellar synthesis of complex organic molecule
• Density Functional Theory Calculations on the Interstellar Formation of Biomolecules