It should be noted here that learning λ GFN1 is itself only of methodological interest, however. Indeed, the conf3 search requires GFN1-xTB for energy ranking, which has a similar computational effort to calculating λ GFN1 . In the following, we therefore exclusively focus on predicting λ DFT , using conf3 for structure generation. To this end we extended our DFT annotated dataset to cover in total 10.900 molecules, randomly drawn from the full hydrocarbon database. The distribution of obtained λ DFT values is shown in Fig S3 . 1.000 molecules served as an external test set for model validation, while at maximum 9.600 of the remaining 9.900 entered the respective training sets.