Rigorous evaluation of extrapolation requires careful preparation of the data for training, validation, and testing. Overlapping solutes structures in the testing and training data are dropped from the training data to avoid data leaks. The astartes software package is used to randomly partition entire experiments into these sets, again ensuring that no solute structures are seen by the model in multiple sets. A single 'model' contains four individual networks trained on a different random training set to account of the affect of random sampling, and a prediction is then the average across these four models.