  The QM9 dataset [Scientific Data, Vol. 1, 140022 (2014)] became a standard
dataset to benchmark machine learning methods, especially on molecular graphs.
It contains geometries as well as multiple computed molecular properties of
133,885 compounds at B3LYP/6-31G(2df,p) level of theory, including frontier
orbitals (HOMO and LUMO) energies. However, the accuracy of HOMO/LUMO
predictions from density functional theory, including hybrid methods such as
B3LYP, is limited for many applications. In contrast, the GW method
significantly improves HOMO/LUMO prediction accuracy, with mean unsigned errors
in the GW100 benchmark dataset of 100 meV. In this work, we present a new
dataset of HOMO/LUMO energies for the QM9 compounds, computed using the GW
method. This database may serve as a benchmark of HOMO/LUMO prediction,
delta-learning, and transfer learning, particularly for larger molecules where
GW is the most accurate but still numerically feasible method. We expect this
dataset to enable the development of more accurate machine learning models for
predicting molecular properties
