# Using Gaussian Process Regression to Simulate the Vibrational Raman Spectra of Molecular Crystals

## TL;DR

This paper demonstrates that Gaussian process regression can efficiently predict vibrational Raman spectra of molecular crystals, reducing computational costs while maintaining high accuracy, and enabling transferability across different polymorphs.

## Contribution

The study introduces a symmetry-adapted GPR framework for predicting vibrational spectra, showing its effectiveness and transferability in molecular crystal simulations.

## Key findings

- Accurate spectra with less than 1000 training points.
- Symmetry-adapted GPR outperforms other GPR variants.
- Model trained on one polymorph predicts spectra of another.

## Abstract

Vibrational properties of molecular crystals are constantly used as structural fingerprints, in order to identify both the chemical nature and the structural arrangement of molecules. The simulation of these properties is typically very costly, especially when dealing with response properties of materials to e.g. electric fields, which require a good description of the perturbed electronic density. In this work, we use Gaussian process regression (GPR) to predict the static polarizability and dielectric susceptibility of molecules and molecular crystals. We combine this framework with ab initio molecular dynamics to predict their anharmonic vibrational Raman spectra. We stress the importance of data representation, symmetry, and locality, by comparing the performance of different flavors of GPR. In particular, we show the advantages of using a recently developed symmetry-adapted version of GPR. As an examplary application, we choose Paracetamol as an isolated molecule and in different crystal forms. We obtain accurate vibrational Raman spectra in all cases with less than 1000 training points, and obtain improvements when using a GPR trained on the molecular monomer as a baseline for the crystal GPR models. Finally, we show that our methodology is transferable across polymorphic forms: we can train the model on data for one structure, and still be able to accurately predict the spectrum for a second polymorph. This procedure provides an independent route to access electronic structure properties when performing force-evaluations on empirical force-fields or machine-learned potential energy surfaces.