As one can see in Figure , the performance of different informative features only minimally varies across the set of 18 systems. On average, using the large feature set resulted in 3.1 ± 0.6 iterations and the small feature set in 3.2 ± 0.6. While the results of the GP performance without chemical information might seem surprising, it must be noted that additional features increase the number of model parameters that need to be learned, as shown in other previously reported active ML studies by Pomberger et al. The model initialization for each single system was conducted with 5% of the training data instead of a consistent number of datapoints. While the number of initialization datapoints varies, the focus is on the relative comparison of the different feature sets. See Table for information on the indexed buffer system positions.