qs$GW$ quasiparticle and $GW$-BSE excitation energies of 133,885 molecules

TL;DR

This paper introduces an extensive dataset of qsGW-BSE excitation energies and quasiparticle energies for over 130,000 molecules, enabling improved machine learning models for excited state properties in chemical sciences.

Contribution

The creation of the large QM9GWBSE dataset, providing high-quality qsGW-BSE data for a wide range of molecules, which was previously unavailable at this scale.

Findings

Dataset includes excitation energies, transition dipole moments, and oscillator strengths.

Enables training of machine learning models for excited state predictions.

Provides a benchmark for future computational chemistry studies.

Abstract

Machine learning applications in the chemical sciences, especially when based on neural networks, critically depend on the availability of large quantities of high quality data. As they provide excellent accuracy for both charged and neutral excitations, a large dataset containing quasiparticle self-consistent GW (qs$GW$) and Bethe-Salpeter equation (BSE) data would be highly desirable to model excited state energies and properties. In this work, we introduce a dataset for qs$GW$-BSE excitation energies and qs$GW$ quasiparticle energies of unprecedented size. Our dataset, denoted QM9GWBSE, supplies $GW$-BSE singlet-singlet and singlet-triplet excitation energies, corresponding transition dipole moments and oscillator strengths as well as qs$GW$ quasiparticle energies for all molecules from the popular QM9 dataset. We anticipate that QM9GWBSE will provide a solid foundation to train highly accurate machine learning models for the prediction of molecular excited state properties.