What question did this study set out to answer?

This work aims to provide a comprehensive dataset of qsGW and GW-BSE excitation energies for molecular studies.

March 13, 2026Scientific Data0 citationsOpen Access

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

DBDario Baum AFArno Förster LVLucas Visscher

Key Points

This work aims to provide a comprehensive dataset of qsGW and GW-BSE excitation energies for molecular studies.
Introduced a large dataset named QM9GWBSE containing excitation energies of 133,885 molecules.
Included data on singlet-singlet and singlet-triplet excitation energies as well as transition dipole moments.
Focused on qsGW quasiparticle energies derived from the QM9 dataset.
Developed a dataset of unprecedented size that supplies GW-BSE and qsGW data.
Anticipated the dataset will enhance the accuracy of machine learning models for excited state predictions.

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 (qsGW) 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 qsGW-BSE excitation energies and qsGW 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 qsGW 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.

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