qsGW quasiparticle and GW-BSE excitation energies of 133,885 molecules
2026-03-10
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 G W ) 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 G W -BSE excitation energies and qs G W quasiparticle energies of unprecedented size. Our dataset, denoted QM9GWBSE, supplies G W -BSE singlet-singlet and singlet-triplet excitation energies, corresponding transition dipole moments and oscillator strengths as well as qs G W 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.