Comparing the corrected with the initial DFTB-D3 landscapes, we can see many of the deficiencies from the baseline (DFTB-D3) landscape have been eliminated. The structures corresponding to the experimentally observed polymorphs are now among the lowest energy structures on the landscape and in the correct expected order of stability, the α polymorph being the global minimum and the β polymorph ranked slightly higher. The CSP structures that were predicted with low energies and high densities by DFTB-D3 have been corrected to higher energies, and now are the highest energy structures on the landscape, emphasizing the very poor description of these structures on the initial landscape. Despite the large correction required, for the vast majority of the landscape the potential estimates the corrections with low uncertainty, the mean uncertainty being 1.51 kJ mol -1 . Moreover, comparing the corrected energies to the calculated PBE-D3 energies for the 300 lowest energy structures on the initial landscape (106 of which were selected by active learning), we find an MAE of 0.4 kJ mol -1 and an RMSE of 0.6 kJ mol -1 (Figure ). The only notable exceptions are the low density CSP structures, which have larger uncertainties. This is due to these structures being some of the highest energy structures on the initial landscape and thus relatively few ended up in the set of candidates for active learning. Nevertheless, despite not training on many of these low density structures, considering the energy range of the landscape the uncertainties are not excessive. Overall, the potential has performed exceedingly well considering the difficulty of the landscape and has notably succeeded in learning the combined intramolecular and intermolecular correction to a high standard. The potentials so far presented have been trained exclusively on energy minima of pre-computed CSP landscapes and consequently have a limited description of the energy surface beyond these points. Here we look at how we can improve the description of PES through on-the-fly training within Monte Carlo simulations. We demonstrate this using the 300 lowest energy structures from the CSP landscape predicted for oxalic acid.