For Figure , we followed the approach from the automatminer library 101 and used automated machine learning, which automatically optimizes over various models and model architectures within a certain computational budget. Concretely, we use the TPOT library, 102-104 which uses genetic programming for all machine learning pipeline steps, including feature engineering (for instance, using principal component analysis). We used the defaults of 100 generations with a population size of 100 but also limited the search time to 48 h and five-fold cross-validation.