Another aspect of the modelling approach quality that is of a particular interest in CSP is the accuracy of the energy ranking predictions. Inference regarding the rank predictions is derived by sampling from the posterior predictive distributions for test structure lattice energies and examining the resulting rank distributions. Figure displays the ranks generated from 10 5 samples drawn from the posterior predictive distribution from training the ARGP model on the 40%/10% training sets. Figures present a closer look at the sampled rank distributions for the first four predicted structures. We find that the known crystal structures α and β are recognised with high certainty as those with the lowest energies. In general, structures in the lower energy region, which is usually of the main interest, are quite well identifiable, unlike those in the middle range, which have larger uncertainties in ranking. This is likely due to the high number of structures with very close PBE0 energy values in the energy region > 8 kJ mol -1 above α (Fig. ).