We first look at the predictions obtained from the first step of the recursive modelling: fitting a GPR model to PBE with force field measurements as a fixed predictor. As per Section 2.5, we use nested maximin designs as training sets. For comparison, we also present results for 30 randomly selected training sets of each size (boxplots in Figure ). The MAE of the mean of the predicted distribution in reproducing the test set PBE lattice energies decreases from just above 2 kJ mol -1 at a 10% training fraction (53 structures for oxalic acid) to 0.5 kJ mol -1 using 70% of structures for training (Figure ), with the error improvements slowing down after the training fraction reaches 40%, which is 210 structures from the oxalic acid data considered. On average, random and maximin training set selection yield similar errors at small (< 30%) training fractions, but for training fractions larger than 30%, the maximin training set selection leads to noticeably smaller errors. This finding agrees with earlier observations for single fidelity GP prediction of lattice energies. The observed relationship between the error and the training fraction holds true for the crystal structure landscapes of all three molecules. The MAE and CRPS both decrease as the training proportion increases (Fig. ), but at a slower rate for maleic hydrazide and urazole than for oxalic acid. The different rates of decrease in the errors probably reflect more complex structural landscapes, particularly of urazole, and more difficult relationships between structural descriptors and lattice energies to model. The MAE and CRPS show similar trends and we shall hereafter evaluate the prediction quality in terms of CRPS. All corresponding MAE values can be found in SI. are reported in the inset of each panel.