Element-focused concatenation: to introduce the next strategy, we consider the following illustrative example. In Fig. , the mean average error (MAE) of a Random Forest model is plotted against the occurrences of the chemical elements in compositions of the training dataset A. Two main patterns can be observed: an increase in MAE as fewer representatives are available at the training stage, and an increase in variance (similar patterns are also observed with other models). As a consequence, one might expect to improve the overall accuracy by populating chemical regions with fewer representatives, while, at the same time, avoiding the introduction of noise that would alter the performances on the rest. In order to do this, we identify the k = 5 elements with the smallest prevalence in A and, for each, we collect n = 10 data points at random containing such element from dataset B. This addresses the weakness of previous concatenation strategy. Although targeting specific classes of elements with a narrow prevalence may be attractive, the presence or absence of a certain single element is not a good proxy for the chemical composition. In fact, this approach ignores any high-level relationship between the involved stoichiometries.