where virtual atoms, are placed on sites of fractional occupancy, with properties intermediate between the real atoms that share the site. While the VCA has been used to calculate some compositionally disordered oxides, it is generally recognised as having important limitations. The bonding properties of compositionally disordered materials often cannot be successfully modelled by averaged virtual atoms, and this can lead to very large divergence between VCA and experiment. An alternative to VCA, the Coherent Potential Approximation (CPA), was introduced in the 1960s, and uses an effective medium to model the average composition of the disordered material, although this approach is computationally expensive, is incompatible with many implementations of DFT, and for some systems cannot give quantitative results. Both VCM and CPA in different ways look to take averages to represent disorder. Alternatively, disorder may be represented only using whole atom occupancies, avoiding fractional occupancies entirely; there are several methods currently in use of this type. The cluster expansion (CE) model began with work to use the Ising model of magnetism to describe compositional order/disorder in the 1950s, and saw major advances from the 1980s 56 onwards. CE considers finite size clusters and computes the properties of the material as a combination of these. Zunger et al. introduced the Special Quasi-random Structure (SRS) as a way of approximating random distributions of atoms within a finite supercell. Grau-Crespo et al used the concept of calculating all possible configurations in a given cell size that represent the total composition of a disordered material. This can be effective for low doping levels, but computation costs increase as the composition of the disordered site approaches 0.5. Very recently, an approach to model the energetics of compositional disorder using machine learning, instead of DFT, has been published. This very brief survey is provided to show that the issues presented by modelling disordered materials are not new. To our knowledge, the Materials Project does not use any of the approaches we mention above to model disorder. While this may provide significant advantages in economy of computation, it admits the possibility that any predicted order will be artificial and not be seen in experiment. That two-thirds of the A-lab 'successful' syntheses are likely unrecognised disordered compounds that have been incorrectly modeled as ordered, seems to imply that this limitation of the Materials Project calculation methodology has not been fully appreciated. Indeed in the Google Deepmind GNoME paper, no mention of compositional disorder is made at all.