Representation sensitivity An interesting question is how to represent a molecule or material. Most of the literature reports use IUPAC names. For ML applications, there has been a lot of effort to represent a chemical with unique line encodings (e.g., SMILES or SELFIES ). As the GPT-3 model has been trained on natural text, one might expect that chemical names are preferred over line representations such as SMILES or SELFIES. Therefore, we investigated different representations for our molecular property prediction tasks (see also Supplementary Note 5). Interestingly, our results (see Supplementary Note 7) show that good results are obtained irrespective of the representation. The fact that we often get the best performance using the IUPAC name of the molecule makes fine-tuning GPT-3 for a particular application relatively simple for non-specialists.