  In recent years, machine learning (ML) surrogate models have emerged as an
indispensable tool to accelerate simulations of physical and chemical
processes. However, there is still a lack of ML models that can accurately
predict molecular vibrational spectra. Here, we present a highly efficient
high-dimensional neural network potentials (HD-NNP) architecture to accurately
calculate infrared (IR) and Raman spectra based on dipole moments and
polarizabilities obtained on-the-fly via ML-molecular dynamics (MD)
simulations. The methodology is applied to pyrazine, a prototypical polyatomic
chromophore. The HD-NNP predicted energies are well within the chemical
accuracy (1 kcal/mol), and the errors for HD-NNP predicted forces are only
one-half of those obtained from a popular high-performance ML model. Compared
to the ab initio reference, the HD-NNP predicted frequencies of IR and Raman
spectra differ only by less than 8.3 cm^(-1), and the intensities of IR spectra
and the depolarizaiton ratios of Raman spectra are well reproduced. The HD-NNP
architecture developed in this work highlights importance of constructing
highly accurate NNPs for predicting molecular vibrational spectra.
