Fragment-based approaches are attractive for CSP as they allow for the introduction of more accurate energy models without the limitations of GGA DFT-D methods. Incorporating fragment-based methods into CSP remains expensive, however, owing to the many two-body terms that must be calculated. To overcome this high computational cost, we investigate using a machine learning method to map force field two-body interaction energies to a higher level of theory. The prediction of many-body terms has been demonstrated for finite systems , indicating that machine learning methods can readily be applied to this task.