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Infrared and Raman spectroscopy are widely used for the characterization ofgases, liquids, and solids, as the spectra contain a wealth of informationconcerning in particular the dynamics of these systems. Atomic scalesimulations can be used to predict such spectra but are often severely limiteddue to high computational cost or the need for strong approximations that limitapplication range and reliability. Here, we introduce a machine learning (ML)accelerated approach that addresses these shortcomings and provides asignificant performance boost in terms of data and computational efficiencycompared to earlier ML schemes. To this end, we generalize the neuroevolutionpotential approach to enable the prediction of rank one and two tensors toobtain the tensorial neuroevolution potential (TNEP) scheme. We apply theresulting framework to construct models for the dipole moment, polarizability,and susceptibility of molecules, liquids, and solids, and show that ourapproach compares favorably with several ML models from the literature withrespect to accuracy and computational efficiency. Finally, we demonstrate theapplication of the TNEP approach to the prediction of infrared and Ramanspectra of liquid water, a molecule (PTAF-), and a prototypical perovskite withstrong anharmonicity (BaZrO3). The TNEP approach is implemented in the free andopen source software package GPUMD, which makes this methodology readilyavailable to the scientific community.

Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra