We present a machine-learning approach toward predicting spectroscopic
constants based on atomic properties. After collecting spectroscopic
information on diatomics and generating an extensive database, we employ
Gaussian process regression to identify the most efficient characterization of
molecules to predict the equilibrium distance, vibrational harmonic frequency,
and dissociation energy. As a result, we show that it is possible to predict
the equilibrium distance with an absolute error of 0.04 {\AA} and vibrational
harmonic frequency with an absolute error of 36 cm−1, including
only atomic properties. These results can be improved by including prior
information on molecular properties leading to an absolute error of 0.02 {\AA}
and 28 cm−1 for the equilibrium distance and vibrational harmonic
frequency, respectively. In contrast, the dissociation energy is predicted with
an absolute error ≲0.4 eV. Alongside these results, we prove that it
is possible to predict spectroscopic constants of homonuclear molecules from
the atomic and molecular properties of heteronuclear. Finally, based on our
results, we present a new way to classify diatomic molecules beyond chemical
bond properties