The lattice energy landscape for maleic hydrazide makes a similar progression (Fig. ), but here the differences between FIT+DMA+GP trained with 10% of dimers and the FIT+DMA+MP2 landscape are more important. This is due to higher errors for maleic hydrazide than oxalic acid at small training fractions; these can be explained by the smaller dataset of 12,522 unique dimers compared to 16,379 in the oxalic acid set (table ) and the larger range of dimer energy corrections that must be learnt for maleic hydrazide (it should also be noted that the descriptor space for maleic hydrazide has considerably more dimensions than oxalic acid). In (b) 96%, 98%, and 96% of the dimers in the known polymorphs are predicted for the MH3 monoclinic, triclinic, and monoclinic forms, respectively. In (c) 80%, 86%, and 74% are predicted for the three polymorphs, respectively (again as chosen deterministically by the max-min algorithm ).