基于中子和质子分离能约束的神经网络对原子核质量的预测OA

Nuclear mass is a fundamental observable value that reflects nuclear structure and stability,and plays a key role in nuclear physics and astrophysics.Most of the existing neural network research focuses on predicting the binding energy or neutron/proton separation energy alone,little attention is paid to the physical correlations between these observable quantities.A physical information-based artificial neural network(ANN)is developed based on the relativistic point-coupling model PCK-PK1 to systematically predict nuclear binding energy and single/double neutron/proton separation energy,while maintaining the physical self-consistency of the predictions.To evaluate the influence of introducing separation-energy constraints,different combinations of loss function weights are used to train the networks,enabling a comparison between networks without separation-energy constraints(such as ANN1)and those containing such constraints(such as ANN3). The neural network significantly improves the overall prediction accuracy of binding energy compared with the PCF-PK1 model.Without separation-energy constraints,ANN1 already achieves high precision for binding energy(RMSE ≈ 0.147 MeV)and separation energy(RMSE ≈ 0.158-0.185 MeV).Incorporating the separation-energy constraints into ANN3 results in a slight improvement in overall prediction accuracy.The binding energy predictions improve by approximately 4.6％,while the separation energy predictions increase by 8.9％12.0％.The improvement is particularly noticeable for nuclei where the deviations of ANN1 predictions from experimental values exceed 0.2 MeV.The datasets presented in this paper are openly available at https://doi.org/10.57760/sciencedb.j00213.00239.