An attractive feature of the fragment-based approach is the inherent parallelizibility of the energy correction. Furthermore, we have shown that the difference between force field and QM dimer interaction energies can be predicted using a Gaussian process, with the dimer geometries described using atomic symmetry functions. Results for two of the most challenging molecules in our study -where differences between force field and MP2 relative energies are greatest -show that lattice energies of the MP2 dimer corrected model can be predicted to within 1-1.5 kJ/mol by training the Gaussian process model on 20% of the dimer energies from a CSP landscape, and that the resulting machine learnt model reproduces the ranking of structures on the fully MP2-corrected landscape. Thus, a 5-fold reduction in the computational cost of the QM correction can be achieved using a simple machine learning approach.