The simulated data was used to train a neural network to predict the error-based descriptor. The 400-element vector of normalized current values were used as input features, and the calculated error-based descriptor was used as the label. Training data was split into 80% train and 20% test sets with variances in error-based descriptor of 3.685 and 3.688, respectively. The mean squared error of a validation set (10% split of train set) was used as the loss function, which was optimized using Adam's optimizer. The neural network had a multi-layer perceptron architecture with a single hidden layer, with a dropout layer prior to the output (10% probability). The hyper-parameters for the model consisted of the number of units in the hidden layer, the activation function of the hidden layer units, and the learning rate of the Adam's optimizer. Hyper-parameters were tuned with a Bayesian-optimization based tuner for 25 trials, performing up to 10 epochs of training for each trial. The best model from the Bayesian-optimization campaign had a learning rate of 0.004, a sigmoidal activation functions, and 192 units in the hidden layer (Figure ). The optimized model then trained for additional 817 epochs with early stopping (Figure , Figure , Table ).