Our results have demonstrated that a single-point QM fragment-based correction leads to improved energetic ranking of observed crystal structures within CSP landscapes. The additional computational cost associated with this correction depends on the QM method used (MP2 is the most promising of those tested thus far), as well as the molecular size and number of dimers required to correct the lattice energy for all low energy crystal structures on the landscape. The number of crystal structures and dimer interaction energies required to correct the lowest 20 kJ/mol of each landscape is listed in Table ; the number of dimers varies widely between molecules, largely due to the differences in numbers of low energy crystal structures on their landscapes. The inherent parallelizability of the fragment-based approach is an advantage to fully periodic QM calculations in terms of computing time because each dimer interaction energy can be assessed simultaneously. However, the cost is large compared to the force field energy evaluations and could become prohibitive if we wanted to optimize crystal structures action energy calculations at each optimization step. Furthermore, the time taken for each calculation could become problematic for larger species, especially for MP2 or higher levels of QM theory. The data-rich nature of the problem makes machine learning an attractive approach to lower the computational expense. Thus, we have investigated using a machine learning model to learn the dimer energy corrections -the difference between force field and QM interaction energies -as a function of the dimer geometry. We use the oxalic acid and maleic hydrazide landscapes to evaluate this approach, as these show the largest ranking and relative energy changes between FIT+DMA and FIT+DMA+MP2 models, so should be the most challenging targets for machine learning the energy corrections.