The FIT+DMA+∆-ML model is not suitable for this task because it is only a correction to the intermolecular contribution to the lattice energy. Therefore, using a dataset derived from the CSP structures selected for the energy correction model, with perturbed atomic coordinates to sample conformational degrees of freedom, we trained a total energy MACE (higher order equivariant message passing neural network) model. Full details are provided in the Supporting Information. The trained MACE model was then applied to geometry optimise the predicted structures of 15 compounds with large differences in molecular geometry between the observed crystal structure and the DFT-optimised molecule (see Figure , bottom three rows). Re-optimisation with the trained MACE model yielded considerable improvements in the geometric agreement of predicted structures with experiment and of their energy ranking on the CSP landscapes (Table ). RMSD 30 between experimental and predicted structures decreased upon reoptimisation for 14 of the 15 compounds, by up to 1.4 Å, and moved the match to the observed structure closer to the global energy minimum in all but 2 cases, with 7 becoming the global energy minimum structure. Figure illustrates the improved geometric agreement for one of these molecules.