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超重核的裂变势垒高度和基态结合能是影响熔合反应中存活概率的关键物理量。它们的准确性是存活概率计算中不确定性的主要来源。在这项工作中，我们应用了迁移学习技术来训练神经网络模型，通过结合理论模型预测数据和实验数据，改进了有限程液滴模型（FRLDM）和WS4理论模型对裂变势垒高度的预测。结果显示，FRLDM模型的均方根误差从1.03 MeV降低到0.60 MeV，WS4模型的均方根误差从0.97 MeV降低到0.61 MeV。对于结合能，我们优化了AME2020的实验值与WS4理论预测之间的差值。在测试集上，均方根误差从0.33 MeV降低到0.17 MeV，而在整个数据集上，它从0.29 MeV降低到0.26 MeV。本研究为提高裂变势垒高度和结合能预测的准确性提供了一种具有前景的方法，这将有助于提高核反应存活概率计算的精度。本文数据集可在https://www.doi.org/10.57760/sciencedb.28388中访问获取 (审稿阶段请通过私有访问链接查看本文数据集https://www.scidb.cn/s/6fmeIz)。This study applies machine learning, specifically transfer learning with neural networks, to improve predictions of fission barrier heights and ground state binding energies of superheavy nuclei, which are crucial for calculating survival probabilities in fusion reactions. Transfer learning for neural networks proceeds in two stages: pre-training and fine-tuning, each driven by a distinct pre-training data set and target data set. In this work we split the pre-training data into 60 % for training and 40 % for validation, while the target data are partitioned into 20 % test, with the remaining 80 % further divided into 60 % training and 40 % validation. To construct the neural-network model we adopt the proton number Z and mass number A as the input layer, employ two hidden layers each containing 128 neurons with ReLU (Rectified Linear Unit) activation, and set the learning rate to 0.001. For the fission-barrier-height model, the pre-training dataset is either the FRLDM or the WS4 model data, and the experimental measurements serve as the target set. For the ground-state binding-energy model, we first form the residuals between WS4 predictions and the AME2020 evaluation, then separate these residuals into a light-nucleus subset and a heavy-nucleus subset according to proton number. The light-nucleus subset is used for pre-training and the heavy-nucleus subset for fine-tuning. After optimization, the root-mean-square error (RMSE) of the FRLDM barrier model falls from 1.03 MeV to 0.60 MeV, and that of the WS4 barrier model drops from 0.97 MeV to 0.61 MeV. For the binding-energy model, the RMSE decreases from 0.33 MeV to 0.17 MeV on the test set and from 0.29 MeV to 0.26 MeV on the full data set. We also present the performance of the fission-barrier model before and after refinement, together with the predicted barrier heights along the isotopic chains of the new elements Z = 119 and Z = 120, and analyzed the reasons for the differences in the results obtained by different models. We hope that these results are intended to provide a useful reference for future theoretical studies. The datasets in this paper are openly available at https://www.doi.org/10.57760/sciencedb.28388(Please use private access link https://www.scidb.cn/s/6fmeIz to access the dataset during the peer review process).
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Keywords:
- Fission barriers /
- Binding energies /
- Machine learning /
- Superheavy nuclei
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