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.

中文翻译：

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使用神经网络的核结合能预测：多层感知器的应用

近年来，人工神经网络及其在大数据集上的应用已成为科学研究的重要组成部分。在这项工作中，我们实现了多层感知器 (MLP)，它是一类前馈人工神经网络 (ANN)，以预测原子核的基态结合能。使用具有三个和四个隐藏层的两种不同的 MLP 架构来研究它们对预测的影响。为了训练 MLP 架构，使用两个不同的输入以及最新的原子质量表，并且还根据输入通道的变化分析结合能预测的变化。可以看出，使用适当的 MLP 架构并将更多的物理信息放入输入通道，MLP 可以对原子核的结合能做出快速可靠的预测，