A Levenberg-Marquardt approach 62 was used to optimize the neural network weights for each AL generation. The initial training set was limited to a single atom configuration and a highly stable configuration. For each of the active learning cycles the network was trained for 2000 epochs each representing a complete training cycle. The training scheme in AENet implements a k-fold cross validation scheme, where only a fraction of structures in the training set are used for objective minimization. The structures not used for the objective minimisation are used to cross validate the network to prevent over fitting, and the network with the least error with the cross validation scheme was chosen to be best network of the cycle and carried forward.