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

In recent years, artificial neural networks and their applications for large data sets have become 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.

Automated summary

This study implements the Multilayer Perceptron (MLP) to predict ground-state binding energies of atomic nuclei, using two different MLP architectures and inputs. The results show that by adding more physical information in the input channels and using appropriate MLP architectures, fast and reliable predictions can be achieved.