Most pertinently, machine learning potentials have been shown to approximate DFT-D lattice energies for crystal structure landscapes to a good degree of accuracy . In the context of CSP, machine learning potentials have typically be trained on data generated using generalised gradient approximation (GGA) DFT functionals, as these have largely remained the standard for accuracy where hybrid functionals or post Hartree-Fock wavefunction methods applied directly to the solid state remain too expensive. However, there are known limitations to GGA functionals , such as delocalization error, which lead to the overstabilization of charge separation 31 , which can lead to serious errors, such as for acid-base co-crystals or salts . Furthermore, hybrid functionals also fail to reproduce the experimental stability order for some challenging polymorphic systems . These discrepancies highlight the need for different energy models to give reliable methods for the full spectrum of organic molecular crystal structures. Machine learning approaches could offer a means to expand the range of available energy models used in CSP without a large increase in computational cost.