Accelerating molecular vibrational spectra simulations with a physically informed deep learning model

Abstract

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.