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

Nuclear binding energy predictions using neural networks: Application of the multilayer perceptron