Transferable ∆-ML lattice energy corrections. To illustrate the use of CSP to train transferable machine-learned energy models, we trained a committee neural network potential using atom-centred symmetry functions for lattice energy corrections to the force field used in this work (FIT+DMA), correcting the lattice energies to the B86bPBE+XDM level. An initial model was trained on 7950 selected crystal structures from ca. 85% of the CSP landscapes (up to 9 crystal structures per landscape), randomly selected from within 8 kJ mol -1 of the global energy minimum of each landscape. This corresponds to just under 5% of the crystal structures (166,395) within this energy range for these landscapes. One crystal structure per landscape was withheld as an in-domain test set, while 10 crystal structures per landscape from the remaining CSP landscapes are used as an extrapolation test set. Following initial training, active learning was applied to identify crystal structures from the training landscapes with highest uncertainty in the lattice energy correction. After two iterations (adding 1000 training structures), a slight decrease in errors was observed in the test set, but no improvement in the extrapolation set (see Supporting Information), so training was halted. We also tried a third iteration of active learning with a wider energy window on each landscape, potentially including more diverse structures, but this did not lower the errors on the test or extrapolation sets. Consequently, we decided to proceed with the model after two iterations of active learning. The performance of the resulting model on the held out test set shows remarkably low errors (Figure ), returning an MAE of just 0.93 kJ mol -1 . Moreover, a similarly low MAE of 1.57 kJ mol -1 is achieved on the extrapolation test set, which contained crystal structures of compounds not included in the training of the correction. Compared to the errors for the baseline FIT+DMA, which returned MAEs of 7.80 and 7.95 kJ mol -1 on the test set and extrapolation set respectively, the correction offers a marked improvement in accuracy. The fact that the errors for the correction are slightly higher on the extrapolation set shows that there are limitations to the transferability of the correction. We expect that better transferability can be achieved as we increase the number of CSP landscapes available for training; the generation of additional landscapes can be targeted to weaknesses in the underlying force field and molecular types where the current machine learned correction has large errors. Although the performance of the correction on the test sets is encouraging, an important question is whether the improved energies are significant enough to produce improved stability rankings of organic crystals. To evaluate the influence on ranking, we applied the correction to all of the CSP landscapes, re-ranking all the predicted structures based on the corrected energies. Thereafter, we compared the ranking of the matches to the experimental structures in terms of both their ranking and their energy above the global minimum with that found using FIT+DMA. Structures with predicted uncertainties greater than 25 kJ mol -1 were omitted from this analysis. This amounted to 257 structures (out of 3.9 million total) with examination indicating the structures were predominately high energy structures containing voids. The resulting distributions (Figure ) over both the training and extrapolation compounds illustrate clearly that the corrected energies in general improve the rankings of the experimental structures. For instance, the correction results in an increase from 424 (FIT+DMA) to 501 (FIT+DMA+∆-ML) of observed structures ranked as global energy minima, and an increase from 767 (FIT+DMA) to 839 (FIT+DMA+∆-ML) within 2 kJ mol -1 of the global energy minimum (the approximate limit of vibrational contributions to free energy differences). These improvements are observed in the CSP landscapes of the extrapolation and training molecules (see Supporting Information). Importantly, the correction also greatly improves the worst ranked experimental matches with the proportion ranked above 35 in energy ranking decreasing by over a third, from 69 (FIT+DMA) to 45 (FIT+DMA+∆-ML) (see Supporting Information).