  We present a data-driven approach for the prediction of the electric dipole
moment of diatomic molecules, which is one of the most relevant molecular
properties. In particular, we apply Gaussian process regression to a novel
dataset to show that dipole moments of diatomic molecules can be learned, and
hence predicted, with a relative error <5%. The dataset contains the dipole
moment of 162 diatomic molecules, the most exhaustive and unbiased dataset of
dipole moments up to date. Our findings show that the dipole moment of diatomic
molecules depends on atomic properties of the constituents atoms: electron
affinity and ionization potential, as well as on (a feature related to) the
first derivative of the electronic kinetic energy at the equilibrium distance.
