With these settings, the active learning converged in 185 structures, corresponding to less than 10% of the candidates and only 1.2% of the total landscape. The final corrected landscape calculated with an 18 member cNNP trained on the final training set is presented in figure , the uncertainties represented by error bars on each structure. Despite the energy cutoff and small training set, the potential achieves good accuracy across the entire landscape: only 9 out of the 14997 structures had uncertainties above 6 kJ mol -1 . The energies of these structures were computed directly with PBE-D3. Pleasingly, the correction yields a considerable reduction in the energy range of the experimentally observed polymorphs, the gap between the global minimum and the low density (α, β and γ) polymorphs reducing to less than 50 kJ mol -1 , which is in line with the solvation stabilization estimated for these structures. Comparing the corrected energies to calculated PBE-D3 energies for 92 of the lowest energy structures on the initial landscape (16 of which were selected by active learning), we find an MAE of 3.1 kJ mol -1 and an RMSE of 4.1 kJ mol -1 (Figure ). In terms of energy rankings, following the correction all five known polymorphs are ranked in the 30 lowest energy structures with the biggest change in rank being observed for the very low energy γ polymorph, which dropped from 647 th to 21 st on the landscape. This remarkably good ranking of the polymorphs with such a minimal training set is highly encouraging for the application of the active learning workflow to other diverse, large scale landscapes, and highlights the advantage of the correction even for landscapes where the low level method is initially thought to perform reasonably well.