Machine-learning prediction of infrared spectra of interstellar
  polycyclic aromatic hydrocarbons
  We design and train a neural network (NN) model to efficiently predict the
infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a
computational cost many orders of magnitude lower than what a first-principles
calculation would demand. The input to the NN is based on the Morgan
fingerprints extracted from the skeletal formulas of the molecules and does not
require precise geometrical information such as interatomic distances. The
model shows excellent predictive skill for out-of-sample inputs, making it
suitable for improving the mixture models currently used for understanding the
chemical composition and evolution of the interstellar medium. We also identify
the constraints to its applicability caused by the limited diversity of the
training data and estimate the prediction errors using a ensemble of NNs
trained on subsets of the data. With help from other machine-learning methods
like random forests, we dissect the role of different chemical features in this
prediction. The power of these topological descriptors is demonstrated by the
limited effect of including detailed geometrical information in the form of
Coulomb matrix eigenvalues.
