  In recent years, artificial neural networks and their applications for large
data sets have became a crucial part of scientific research. In this work, we
implement the Multilayer Perceptron (MLP), which is a class of feedforward
artificial neural network (ANN), to predict ground-state binding energies of
atomic nuclei. Two different MLP architectures with three and four hidden
layers are used to study their effects on the predictions. To train the MLP
architectures, two different inputs are used along with the latest atomic mass
table and changes in binding energy predictions are also analyzed in terms of
the changes in the input channel. It is seen that using appropriate MLP
architectures and putting more physical information in the input channels, MLP
can make fast and reliable predictions for binding energies of atomic nuclei,
which is also comparable to the microscopic energy density functionals.
