applicability to structural and ground state properties. Owing to the high computational cost of GW +BSE, such datasets are scarce and the amount of data they contain is relatively small compared to DFT datasets. We note that the GW datasets cited here comprise small isolated molecules, which are considerably faster to calculate than periodic molecular crystals with hundreds of atoms in the unit cell. Recently, ML has been applied to predict the GW quasiparticle energies of small molecules. It is challenging to construct transferable ML models based on "small data". This has limited the applicability of ML to excited state properties of molecular crystals. Emerging approaches to ML with small data include multi-fidelity approaches.