As pure mathematical functions, neural networks do not natively estimate uncertainties in their predictions and thus various methods have been developed to provide these. A common approach with NNPs, and the one we use here, is to create committee neural network potentials (cNNP) and obtain uncertainties via QbC. This involves training an ensemble of individual models, the committee, using the same dataset but with random variations in the initialisation of each member. Predictions are then made by averaging over the predictions of the individual members; for example energies are estimated as