We now conduct some experiments on a dummy task to test how well the GPT-3 model can extrapolate to HOMO-LUMO gaps for which it has not received any training. To mimic this situation, we retrained our inverse design model using a dataset that only has molecules with HOMO-LUMO gaps smaller than 3.5 eV, and subsequently query the model with a question that requires the GPT-3 model to extrapolate (and, e.g., to find that very small molecules are associated with large HOMO-LUMO gaps; a task we only selected for demonstration purposes and that can be exploited by generating small molecules). We do this by asking more than 1,000 times the question: What is a molecule with a HOMO LUMO gap of <XX>, where each time we slightly change the value of the HOMO LUMO gap, i.e., we sample XX from a Gaussian centered at 4 eV. Interestingly, the GPT-3 model does provide structures with a distribution of which our quantum calculations confirm that a significant fraction has a HOMO-LUMO gap > 4.0 eV. Again, this is a remarkable result. In our training set, there was not a single molecule with a band gap > 3.5 eV, which shows that the GPT-3 model can make extrapolations. We can do a similar experiment for the photoswitches, for which we might have a library of photoswitches whose transition wavelengths are all below 350 nm. For practical applications, however, it can often be essential to have adsorption at larger wavelengths. In this case, we can successfully use a fine-tuned GPT-3 model to generate photoswitch molecules that adsorb at lower energy (Supplementary Fig. , which we also validated with TD-DFT in Supplementary Note 12.2.2).