Machine Learned Interatomic Potentials. One of the more straightforward applications of machine learning in CSP is for the prediction of high quality lattice energies, to reduce the cost of geometry optimisations or of the final energy ranking of structures.(69) Such approaches have been demonstrated in molecular organic CSP by training models on the landscapes of individual molecules. The CSP dataset developed in this work has significant potential for training data-derived models for organic crystals that could be applied more broadly.