The final key insight from the MatFold analysis stems from the dependence of expected MAE on both C K and D. Figure plots expected MAE for D = {0.1, 0.5, 1.0}, expressed on the x-axis in units of number of defect examples in the training data. Data leakage and underestimation of expected MAE are even more pronounced for the smallest dataset, and the rapid plateauing of the expected MAE with increasing data is potentially indicative of the absolute accuracy limit of the model. For more realistic screening criteria, i.e., C K = {Composition, Structure, Chemsys}, large accuracy gains are and will continue to be obtained with increasing data collection. Interestingly (and perhaps intuitively), for C K = Chemsys the improvement qualitatively appears to be saturating before the other criteria, but will only be confirmed with additional data collection. Finally, C K = Elements reveals that additional data collection does not increase the accuracy during inference on compounds containing unseen elements. In fact, the error slightly increased with additional data collection because it may have introduced compounds with new test set elements which are even more difficult to extrapolate to from the elements contained in the train set (see Supplementary Information for more details).