through $N_{\rm max} = 10$ was used for the training stage of the ANN. The training data was limited to $N_{\rm max} = 10$ and below since future applications to heavier nuclei will likely not have data at higher $N_{\rm max}$ values due to exponential increase in the matrix dimension. This $N_{\rm max} \leq 10$ data set was randomly divided into two separate sets using the *dividerand* function in MATLAB: 85% for the training set and 15% for the testing set. A *back-propagation* algorithm with Bayesian regularization with MSE performance function was used for ANN training. Bayesian regularization does not require a validation data set. For function approximation, Bayesian regularization provides better generalization performance than early stopping in most cases, but it takes longer to converge. The performance improvement is more noticeable when the data set is small because Bayesian regularization does not require a validation data set, leaving more data for training. In MATLAB, Bayesian regularization has been implemented in the function *trainbr*. When using *trainbr*, it is important to train the network until it reaches convergence. In this study, the training process is stopped if: (1) it reaches the maximum number of iterations, 1000; (2) the performance has 