Where, e.g. #A-A accounts for the number of homoatomic bonds between atom type A. The summed mass (M ) is appended to account for compositional changes. The ML model is trained on relaxed nanoparticles, though predictions are based on features generated for the unrelaxed structure. The set of descriptors generated in the fingerprint vector are invariant to small changes to the coordinate system, such as a small expansion or contraction of the lattice resulting from the geometry relaxation. A similar ∆-learning method, has been discussed by von Lilienfeld et al. Within the ML accelerated GA (MLaGA) implementation, exists two tiers of energy evaluation, one by the ML functions giving a predicted fitness and the other by the energy calculator providing the actual fitness. A nested GA has been implemented to search the surrogate model representation, generated by the ML. This acts as a high throughput screening function based solely on predicted fitness, running in the "master" GA. The nested surrogate GA takes the current population and is able to progress through additional search iterations, where evaluation and selection are based only on the current model of the PES. The final population from the nested GA returns unrelaxed candidates to the master GA. This is well suited to making large steps on the PES without performing expensive energy evaluations. A difficulty when searching with the MLaGA is that convergence criteria typically used in these studies is no longer suitable. The MLaGA methodology is specifically implemented to limit the number of energy evaluations that are performed. Therefore, every candidate in the generation typically progresses the population. This progression within the population continues until the ML routine is unable to find new candidates that are predicted to be better, essentially stalling the search. For this reason, convergence is considered to have been achieved by the point at which the ML routine prevents new candidates from being evaluated. The general MLaGA methodology is shown in Figure .