Exploring the application of neural networks in predictions of nuclear binding energies

Abstract: In this project the locations of the proton and neutron drip-lines are predicted using neural networks and theoretical data obtained by applying the HFBTHO program. For each of the neural networks a comparison is made between neural network predictions and experimental data in the region experimental data exists. By comparing the effectiveness of the networks at reproducing experimental results with the effectiveness of the HFBTHO program it is found that extensive improvements can be made these results. This indicates that the application of machine learning exists as a potential method for making corrections to theoretical modes. Whether the final predictions are sufficiently trustworthy to reach a conclusion is difficult to determine, however this seems to be a potential path for future development into obtaining data.