The yield is determined using a Bayesian Neural Network (BNN) that is trained on the experimental data from Baumgartner et al. The BNN is implemented in PyTorch using the BLITZ library. The total dataset contains 96 experiments, where 86 are used for training and 10 for test. The model is trained using 10-fold cross validation. As shown in Figure , a mean absolute error of 0.08 is achieved on the test set. The cost is determined using data from chemical suppliers, as shown in Table and Table . For the bases, we use data supplied by Baumgartner et al., and all other data is determined by us.