.png){kind=icon}
返回
Data-driven density functional model for atomic nuclei
.png){kind=icon}
PRE
AI
DOI：10.1103/PhysRevC.109.064312.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Through ensemble learning with multitasking and complex connection neural networks, we aggregated nuclear properties, including ground-state density distributions, charge radii, and binding energies obtained from the Kohn-Sham auxiliary single-particle systems. The root-mean-squared (RMS) error in describing binding energies is reduced to about 500 keV, while the RMS error in describing charge radii is decreased to about 0.017 fm. In addition, by using the correlation between densities and binding energies, we discuss the impact of various densities and nuclear radii on the binding energy and find the neutron -skin thickness of 208 Pb to be around 0 . 220 fm. The present paper provides a new way to accelerate the integration of machine learning into nuclear density functional theory.
期刊
IF:
3.4
论文数:
2.9W
被引数:
5.7W
机构
引用论文
Global performance of covariant energy density functionals: Ground state observables of even-even nuclei and the estimate of theoretical uncertainties.png)
PHYSICAL REVIEW C
OAAI
PHYSICAL REVIEW CIF3.4
OAAI分享收藏The thermal structure of Jupiter II. Observations and analysis of 8–14 micron radiationIcarus
IcarusIF0
PREAI
分享收藏Nuclear mass predictions with machine learning reaching the accuracy required by r-process studiesPHYSICAL REVIEW C
OAAI
PHYSICAL REVIEW CIF3.4
OAAI分享收藏Nuclear masses in extended kernel ridge regression with odd-even effects具有奇偶效应的扩展核岭回归中的核质量PHYSICS LETTERS BOAAI
PHYSICS LETTERS BIF4.5
OAAI分享收藏Calibration of nuclear charge density distribution by back-propagation neural networksPHYSICAL REVIEW COAAI
PHYSICAL REVIEW CIF3.4
OAAI分享收藏
查看更多内容