Deep Learning for UV Absorption Spectra with SchNarc: First Steps Towards Transferability in Chemical Compound Space
Julia Westermayr, Philipp Marquetand

TL;DR
This paper demonstrates how machine learning models, specifically an extended SchNarc approach, can predict excited-state properties and UV absorption spectra, showing promising transferability across different molecules in chemical space.
Contribution
It introduces an extension of the SchNarc method to model excited-state dipole moments and explores the transferability of these models to unseen molecules.
Findings
ML models can predict excited-state properties for molecules outside training set
Transferability of excited-state ML models is feasible across different molecules
ML can accurately reproduce UV absorption spectra from excited-state energies
Abstract
Machine learning (ML) has shown to advance the research field of quantum chemistry in almost any possible direction and has recently also entered the excited states to investigate the multifaceted photochemistry of molecules. In this paper, we pursue two goals: i) We show how ML can be used to model permanent dipole moments for excited states and transition dipole moments by adapting the charge model of [Chem. Sci., 2017, 8, 6924-6935], which was originally proposed for the permanent dipole moment vector of the electronic ground state. ii) We investigate the transferability of our excited-state ML models in chemical space, i.e., whether an ML model can predict properties of molecules that it has never been trained on and whether it can learn the different excited states of two molecules simultaneously. To this aim, we employ and extend our previously reported SchNarc approach for…
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