A Kohn-Sham scheme based neural network for nuclear systems
A Kohn-Sham scheme based neural network for nuclear systems
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DOI：
10.1016/j.physletb.2023.137870
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发表时间：
2022-12
影响因子：
4.4
通讯作者：
Zu-Xing Yang;Xiao-Hua Fan;Zhi-Pan Li;H. Liang
中科院分区：
文献类型：
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作者：
Zu-Xing Yang;Xiao-Hua Fan;Zhi-Pan Li;H. Liang
详细阐述了基于 Kohn-Sham 方案的多任务神经网络，用于核壳演化的监督学习。训练集由单粒子波函数和 320 个原子核的占据概率组成，由 Skyrme 密度泛函理论计算。结果发现，推导的密度分布、动量分布和电荷半径与未经训练的原子核的基准测试结果非常吻合。特别是，完成壳层演化可以显着改善核密度的外推。经过进一步基于电荷半径的校准后，网络演化出了更强的预测能力。这使得通过结合核复杂系统的实验数据来推断可观测值之间的相关性成为可能。
A Kohn-Sham scheme based multi-task neural network is elaborated for the supervised learning of nuclear shell evolution. The training set is composed of the single-particle wave functions and occupation probabilities of 320 nuclei, calculated by the Skyrme density functional theory. It is found that the deduced density distributions, momentum distributions, and charge radii are in good agreements with the benchmarking results for the untrained nuclei. In particular, accomplishing shell evolution leads to a remarkable improvement in the extrapolation of nuclear density. After a further charge-radius-based calibration, the network evolves a stronger predictive capability. This opens the possibility to infer correlations among observables by combining experimental data for nuclear complex systems.