# Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules

## TL;DR

This paper introduces a machine learning method to simulate infrared spectra of molecules in solvents, accurately capturing how the solvent affects the results.

## Contribution

A multiscale ML/MM framework that efficiently predicts IR spectra with high fidelity and captures solvent-driven vibrational shifts.

## Key findings

- The ML/MM approach reproduces experimental IR spectra with high accuracy.
- The method efficiently captures solvent effects on vibrational shifts.
- It provides a computationally efficient route for vibrational spectroscopy simulations.

## Abstract

We introduce a multiscale
machine-learning molecular
dynamics (MD)
strategy for simulating infrared spectra of solvated molecules. Our
approach integrates an efficient sampling of environmental configurations
with a hierarchical model that predicts forces and dipole moments
as analytical derivatives of the energy, allowing IR spectra simulations
from MD trajectories. Solvent effects are incorporated through a molecular
mechanics (MM) representation of the environment embedded within the
ML description of the solute. Applied to representative biorelated
systems, the resulting ML/MM framework reproduces experimental spectra
with high fidelity and accurately captures solvent-driven vibrational
shifts. This approach provides a computationally efficient and robust
route for describing solvent effects in vibrational spectroscopy.