We first outline the fragment-based method used is the study, which combines anisotropic atom-atom force fields with a range of QM methods. Following similar work in comparing energy models for CSP , the energy model is tested by monitoring the ranking of observed crystal structures within CSP structure sets for a range of molecules when calculating each two-body term explicitly. We then describe a machine learning approach to learning the difference between force field and QM dimer energies, which reduces the cost of the fragment based QM correction. The results of our machine learnt energy model are assessed by comparison to explicit QM calculations.