In the case of the photoswitches, we have seen that the GPT-3 model can generate new molecules quite different from the training set. To explore in detail how far we can stretch the limits of what new molecules we can generate, we choose an application for which quantum calculations are known to predict the experimental values sufficiently accurately. The HOMO-LUMO gap is such an application. The HOMO-LUMO gap is relevant, for instance, in electronic applications that aim to excite a molecule at a specific energy. This HOMO-LUMO gap can be predicted accurately using semi-empirical quantum mechanics (GFN2-xTB ), which is computationally affordable enough for us to compute for all generated molecules (see Supplementary Fig. ). Moreover, the QMugs dataset, has listed these HOMO-LUMO calculations for 665 k molecules.