The complete data set produced from AIMD trajectories features a total of 1 189 836 frames. We compared two approaches: random sampling and the flat-histogram approach. In the random sampling approach, configurations are taken at random from our very long AIMD trajectories, and then split randomly between training and test data. Due to the very large nature of our AIMD trajectories (four trajectories of more than 50 ps), the configurations available are widely spaced in time and there is no need to use specific methods to avoid correlation between configurations. In the flat-histogram approach, we first bin with respect to energy (with the number of bins corresponding to the requested number of samples), and then a random sample is taken from each bin. The resulting difference in energy distributions for the two different approaches is demonstrated in Figure . This process is completed without replacement , and repeated, to give a 'training' data set and the leftover frames were subsequently sampled in the same way to give a 'test' set (data binning is repeated and unique for each data set). The unsampled frames were saved as 'validation'. An initial train-test data set was produced for both the random sampling approach for 1 000 training samples and 100 test samples for hyperparameter screening, and a final train-test data set produced with the flat-histogram approach for 2 500 training samples and 200 test samples.