Spectroscopy from Machine Learning by Accurately Representing the Atomic Polar Tensor

TL;DR

This paper introduces a machine learning approach using E(3)-equivariant neural networks to accurately predict atomic polar tensors, enabling detailed IR spectral analysis of liquids like water with reduced computational cost.

Contribution

The methodology is general, transferable, and overcomes computational bottlenecks in calculating IR spectra from molecular dynamics simulations.

Findings

Achieved excellent agreement with reference IR spectra for liquid water.

Demonstrated the transferability of the approach to different systems.

Provided a new route for microscopic interpretation of vibrational spectra.

Abstract

Vibrational spectroscopy is a key technique to elucidate microscopic structure and dynamics. Without the aid of theoretical approaches, it is however, often difficult to understand such spectra at a microscopic level. Ab initio molecular dynamics have repeatedly proved to be suitable for this purpose, however, the computational cost can be daunting. Here, the E(3)-equivariant neural network e3nn is used to fit the atomic polar tensor of liquid water a posteriori on top of existing molecular dynamics simulations. Notably, the introduced methodology is general and thus transferable to any other system as well. The target property is most fundamental, gives access to the IR spectrum and, more importantly, it is a highly powerful tool to directly assign IR spectral features to nuclear motion -- a connection which has been pursued in the past but only using severe approximations due to the prohibitive computational cost. The herein introduced methodology overcomes this bottleneck. To benchmark the machine learning model, the IR spectrum of liquid water is calculated, indeed showing excellent agreement with the explicit reference calculation. In conclusion, the presented methodology gives a new route to calculate accurate IR spectra from molecular dynamics simulations and will facilitate the understanding of such spectra on a microscopic level.