# Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra

## TL;DR

This paper introduces a machine learning method called TNEP to efficiently simulate infrared and Raman spectra for molecules and materials.

## Contribution

The novel TNEP framework generalizes neuroevolution potentials to predict tensorial properties with improved accuracy and efficiency.

## Key findings

- TNEP models outperform existing ML methods in predicting dipole moment, polarizability, and susceptibility.
- The approach successfully predicts infrared and Raman spectra for liquid water, PTAF–, and BaZrO3.
- TNEP is implemented in the open-source software gpumd for broad accessibility.

## Abstract

Infrared and Raman
spectroscopy are widely used for the characterization
of gases, liquids, and solids, as the spectra contain a wealth of
information concerning, in particular, the dynamics of these systems.
Atomic scale simulations can be used to predict such spectra but are
often severely limited due to high computational cost or the need
for strong approximations that limit the application range and reliability.
Here, we introduce a machine learning (ML) accelerated approach that
addresses these shortcomings and provides a significant performance
boost in terms of data and computational efficiency compared with
earlier ML schemes. To this end, we generalize the neuroevolution
potential approach to enable the prediction of rank one and two tensors
to obtain the tensorial neuroevolution potential (TNEP) scheme. We
apply the resulting framework to construct models for the dipole moment,
polarizability, and susceptibility of molecules, liquids, and solids
and show that our approach compares favorably with several ML models
from the literature with respect to accuracy and computational efficiency.
Finally, we demonstrate the application of the TNEP approach to the
prediction of infrared and Raman spectra of liquid water, a molecule
(PTAF–), and a prototypical perovskite with strong
anharmonicity (BaZrO3). The TNEP approach is implemented
in the free and open source software package gpumd, which
makes this methodology readily available to the scientific community.