Machine Learned Interatomic Potentials. To investigate the potential of the CSP dataset to train data-derived models, a subset of the predicted crystal structures with energies within 8.0 kJ mol -1 of the global energy minimum on their CSP landscape was selected for training a lattice energy correction to the FIT+DMA force field. The subset was determined by active learning via query-by-committee using a committee of eight high-dimensional neural network potentials (NNPs), with selected structures evaluated by DFT+D single points at the B86bPBE+XDM level. From this, a dataset of the crystal structures and the corresponding energy correction between the FIT+DMA and B86bPBE+XDM lattice energies (∆E) was created. B86bPBE+XDM lattice energies were calculated as the total energy less the energies of the isolated molecules from the unit cell calculated with the same basis set and tolerances. The initial dataset (before the active learning iterations) was generated by randomly selecting up to 10 low energy predicted crystal structures for each compound, resulting in a dataset of 10,249 structures approximately evenly distributed across the compounds. To evaluate transferability, the total dataset was partitioned into a training dataset consisting of the CSP structures for a randomly selected ca. 85% of the compounds and an extrapolation test set consisting of CSP structures for the remaining compounds. A further in-domain test set was formed by randomly extracting one structure per compound from the training set. The NNPs were then trained on the remaining training dataset to yield a ∆-ML model capable of predicting the lattice energy correction. The standard deviation between the ensemble of NNPs was used to estimate the uncertainty of predictions, and was exploited in the active learning iterations to add high uncertainty candidates from the remaining low energy predicted structures of the training compounds, which overall added a further 1000 structures to the training set. Corrected CSP landscapes were calculated using the final model by adding predicted lattice energy corrections to the FIT+DMA energies (FIT+DMA+∆-ML). Additionally, for performing unconstrained geometry optimisations MACE equivariant message-passing neural network (MPNN) models were trained using a dataset derived from that of the NNP correction model. Further details of the datasets and machine learning models are provided in the supporting information.