To create structural models that incorporate this partial disorder, we used a genetic algorithm (GA) structureprediction scheme to generate a set of exemplar 4 × 4 × 4 supercells. For each structure-prediction calculation, we initialised the genetic algorithm with a starting population of 40 4×4×4 TiOF 2 supercells, each with random O and F anion configurations. The genetic algorithm used a combination of elitist selection and proportional selection, employing a Boltzmann-weighted fitness function, f i ∝ exp(E i /kT ), to select structures from each generation for seeding the next generation (full details of the GA algorithm are given in the Supporting Information). The energies for each structure considered by the algorithm were calculated using our DFT-derived cluster expansion model. This genetic algorithm structure-prediction scheme is conceptually similar to running multiple concurrent Monte-Carlo-based simulated annealing simulations, where structural motifs associated with low energy configurations at any point can be shared across simulations. The GA-algorithm quickly filters out high-energy, less probable structures to produce a pool of structures with energetically reasonable O/F anion configurations (Fig. ).