UV-Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning

TL;DR

This paper introduces a machine learning approach trained on quantum chemical calculations to accurately predict UV-visible absorption spectra of solvated aromatic molecules, significantly reducing computational costs while maintaining high accuracy.

Contribution

The study presents a novel workflow combining quantum chemistry and machine learning to efficiently predict UV-visible spectra, focusing on aromatic molecules and revealing the physical basis of excitations.

Findings

ML model predicts spectra with less than 0.1 eV deviation from experiments

ML captures the atomic environment of phenyl rings related to electronic transitions

Workflow accelerates UV-visible spectra calculations for molecular systems

Abstract

Predicting UV-visible absorption spectra is essential to understanding photochemical processes and designing energy materials. Quantum chemical methods can deliver accurate calculations of UV-visible absorption spectra, but they are computationally expensive, especially for large systems or when one computes line shapes from thermal averages. Here, we present an approach to predicting UV-visible absorption spectra of solvated aromatic molecules by quantum chemistry (QC) and machine learning (ML). We show that a ML model, trained on the high-level QC calculation of the excitation energy of a set of aromatic molecules, can accurately predict the line shape of the lowest-energy UV-visible absorption band of several related molecules with less than 0.1 eV deviation with respect to reference experimental spectra. Applying linear decomposition analysis on the excitation energies, we unveil that our ML models probe vertical excitations of these aromatic molecules primarily by learning the atomic environment of their phenyl rings, which align with the physical origin of the $\pi\rightarrow\pi^\star$ electronic transition. Our study provides an effective workflow that combines ML with quantum chemical methods to accelerate the calculations of UV-visible absorption spectra for various molecular systems.