Machine learning the nuclear mass
文献类型：期刊论文
| 作者 | Gao, Ze-Peng3,4; Wang, Yong-Jia3; Lu, Hong-Liang2; Li, Qing-Feng1,3; Shen, Cai-Wan3; Liu, Ling4 |
| 刊名 | NUCLEAR SCIENCE AND TECHNIQUES
 |
| 出版日期 | 2021-10-01 |
| 卷号 | 32期号:10页码:13 |
| ISSN号 | 1001-8042 |
| 关键词 | Nuclear mass Machine learning Binding energy Separation energy |
| DOI | 10.1007/s41365-021-00956-1 |
| 通讯作者 | Wang, Yong-Jia(wangyongjia@zjhu.edu.cn) ; Li, Qing-Feng(liqf@zjhu.edu.cn) |
| 英文摘要 | Background: The masses of similar to 2500 nuclei have been measured experimentally; however, >7000 isotopes are predicted to exist in the nuclear landscape from H (Z = 1) to Og (Z = 118) based on various theoretical calculations. Exploring the mass of the remaining isotopes is a popular topic in nuclear physics. Machine learning has served as a powerful tool for learning complex representations of big data in many fields. Purpose: We use Light Gradient Boosting Machine (LightGBM), which is a highly efficient machine learning algorithm, to predict the masses of unknown nuclei and to explore the nuclear landscape on the neutron-rich side from learning the measured nuclear masses. Methods: Several characteristic quantities (e.g., mass number and proton number) are fed into the LightGBM algorithm to mimic the patterns of the residual delta(Z,A) between the experimental binding energy and the theoretical one given by the liquid-drop model (LDM), Duflo-Zucker (DZ, also dubbed DZ28) mass model, finite-range droplet model (FRDM, also dubbed FRDM2012), as well as the Weizsacker-Skyrme (WS4) model to refine these mass models. Results: By using the experimental data of 80% of known nuclei as the training dataset, the root mean square deviations (RMSDs) between the predicted and the experimental binding energy of the remaining 20% are approximately 0.234 +/- 0.022, 0.213 +/- 0.018, 0.170 +/- 0.011, and 0.222 +/- 0.016 MeV for the LightGBM-refined LDM, DZ model, WS4 model, and FRDM, respectively. These values are approximately 90%, 65%, 40%, and 60% smaller than those of the corresponding origin mass models. The RMSD for 66 newly measured nuclei that appeared in AME2020 was also significantly improved. The one-neutron and two-neutron separation energies predicted by these refined models are consistent with several theoretical predictions based on various physical models. In addition, the two-neutron separation energies of several newly measured nuclei (e.g., some isotopes of Ca, Ti, Pm, and Sm) predicted with LightGBM-refined mass models are also in good agreement with the latest experimental data. Conclusions: LightGBM can be used to refine theoretical nuclear mass models and predict the binding energy of unknown nuclei. Moreover, the correlation between the input characteristic quantities and the output can be interpreted by SHapley additive exPlanations (a popular explainable artificial intelligence tool), which may provide new insights for developing theoretical nuclear mass models. |
| WOS关键词 | GROUND-STATE MASSES ; DEFORMATIONS ; ENERGY |
| 资助项目 | National Science Foundation of China[U2032145] ; National Science Foundation of China[11875125] ; National Science Foundation of China[12047568] ; National Science Foundation of China[11790323] ; National Science Foundation of China[11790325] ; National Science Foundation of China[12075085] ; National Key Research and Development Program of China[2020YFE0202002] ; ``Ten Thousand Talent Program'' of Zhejiang Province[2018R52017] |
| WOS研究方向 | Nuclear Science & Technology ; Physics |
| 语种 | 英语 |
| 出版者 | SPRINGER SINGAPORE PTE LTD |
| WOS记录号 | WOS:000705844800002 |
| 资助机构 | National Science Foundation of China ; National Key Research and Development Program of China ; ``Ten Thousand Talent Program'' of Zhejiang Province |
| 源URL | [http://119.78.100.186/handle/113462/136153]  |
| 专题 | 中国科学院近代物理研究所 |
| 通讯作者 | Wang, Yong-Jia; Li, Qing-Feng |
| 作者单位 | 1.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China 2.Huawei Technol Co Ltd, HiSilicon Res Dept, Shenzhen 518000, Peoples R China 3.Huzhou Univ, Sch Sci, Huzhou 313000, Peoples R China 4.Shenyang Normal Univ, Coll Phys Sci & Technol, Shenyang 110034, Peoples R China |
| 推荐引用方式 GB/T 7714 | Gao, Ze-Peng,Wang, Yong-Jia,Lu, Hong-Liang,et al. Machine learning the nuclear mass[J]. NUCLEAR SCIENCE AND TECHNIQUES,2021,32(10):13. |
| APA | Gao, Ze-Peng,Wang, Yong-Jia,Lu, Hong-Liang,Li, Qing-Feng,Shen, Cai-Wan,&Liu, Ling.(2021).Machine learning the nuclear mass.NUCLEAR SCIENCE AND TECHNIQUES,32(10),13. |
| MLA | Gao, Ze-Peng,et al."Machine learning the nuclear mass".NUCLEAR SCIENCE AND TECHNIQUES 32.10(2021):13. |
入库方式： OAI收割
来源：近代物理研究所
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