# Nuclear mass predictions based on convolutional neural network Source: [Nuclear mass predictions based on convolutional neural network]() Collection index: [llms.txt]() Data updated: 2025-10-25T08:35:04.349Z Authors: Yanhua Lu, Tianshuai Shang, Pengxiang Du, Jian Li, Haozhao Liang, Zhongming Niu Published: 2024-04-23T11:45:23.000Z ## Abstract A convolutional neural network (CNN) is employed to investigate nuclear mass. By introducing the masses of neighboring nuclei and the paring effects at the input layer of the network, local features of the target nucleus are extracted to predict its mass. Then, through learning the differences between the experimental nuclear masses and the predicted nuclear masses by the WS4 model, a new global-local model (CNN-WS4) is developed, which incorporates both the global nuclear mass model and local features. Due to the incorporation of local features, the CNN-WS4 model achieves high accuracy on the training set. When extrapolating for newly emerged nuclei, the CNN-WS4 also exhibits appreciable stability, thereby demonstrating its robustness. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Nuclear mass prediction on AME 2020](): Nuclear mass prediction; AME 2020; ExtRMSE: 0.211. ## Original sources and implementation [Original paper]() [PDF]()