2026-10-10T16:52:11Z https://www.scidb.cn/oai

10.57760/sciencedb.011au 2026-09-03T13:29:21Z

2026-09-03 Calculated Results of $(n,2n)$ and $(n,3n)$ Reaction Excitation Functions Using a Combined Systematics and Neural Network Method doi:10.57760/sciencedb.011au en This dataset provides predicted excitation functions for neutron-induced (n,2n) and (n,3n) reactions calculated using the NN+SYS approach, which combines systematics with neural networks. The nuclides included in the dataset are consistent with those investigated in the corresponding study. The NN+SYS approach is based on physically motivated systematics formulas, while the neural network is used to optimize the parameters of the systematics rather than to predict unconstrained reaction cross sections directly. Physical mechanisms including reaction thresholds, pre-equilibrium emission, and competition among neutron-emission channels are incorporated into the model, thereby improving the prediction accuracy while preserving the essential physical characteristics of the excitation functions. The data used for model development were mainly obtained from the EXFOR experimental database, with evaluated nuclear data from ENDF/B-VIII.1 and JENDL-5 used to supplement energy regions where experimental data are insufficient. The original data were subjected to outlier screening, consistency checks, and training/test splitting before being used to train the NN+SYS model. The trained model was then used to calculate the reaction cross sections of each nuclide as a function of incident neutron energy.The data released in this dataset include only the NN+SYS predictions for the nuclides investigated in the study. Each row in the data table represents an NN+SYS prediction for a specific target nuclide at a given incident neutron energy. The main fields include the proton number Z, mass number A, incident neutron energy E, and the reaction cross section predicted by NN+SYS. Both Z and A are dimensionless integers. The incident neutron energy E is given in MeV, and the predicted reaction cross section is given in barns (b). Multiple records are provided for the same nuclide at different incident neutron energies, and these data points collectively constitute the corresponding (n,2n) or (n,3n) reaction excitation function. Energy; Systematics; Neural network Qianhui Yang Yangyang Liu Jimin Wang EMBARGO https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank